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  • Articles  (87)
  • machine learning  (48)
  • stability  (39)
  • Springer  (87)
  • Blackwell Publishing Ltd
  • 1995-1999  (83)
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  • Computer Science  (87)
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  • Articles  (87)
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  • 1
    Electronic Resource
    Electronic Resource
    On the stability of multistep formulas for Volterra integral equations of the second kind (1980)
    Springer
    Computing 24 (1980), S. 341-347 
    Details
    ISSN: 1436-5057
    Keywords: Numerical analysis ; Volterra integral equations of the second kind ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Ziel dieser Arbeit ist es, die Stabilitätseigenschaften einer Klasse Volterrascher Integralgleichungen zweiter Art zu untersuchen. Unsere Behandlung ist der üblichen Stabilitätsanalyse ähnlich, in der die Kernfunktionen zu einer im voraus beschränkten Klasse von Testfunktionen gehören. Wir haben die Klasse der “endlich zerlegbaren” Kerne betrachtet. Stabilitätsbedingungen werden abgeleitet und verglichen mit den Bedingungen für die einfache Testgleichung. Es zeigt sich, daß die neuen Kriteria einschränkender sind als die konventionellen Bedingungen. Der praktische Wert wird getestet durch numerische Experimente mit der Trapezregel.
    Notes: Abstract The purpose of this paper is to analyse the stability properties of a class of multistep methods for second kind Volterra integral equations. Our approach follows the usual analysis in which the kernel function is a priori restricted to a special class of test functions. We consider the class of finitely decomposable kernels. Stability conditions will be derived and compared with those obtained with the simple test equation. It turns out that the new criteria are more severe than the conventional conditions. The practical value is tested by numerical experiments with the trapezoidal rule.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02237819
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  • 2
    Electronic Resource
    Electronic Resource
    A note on stability investigations for Rosenbrock-type methods for quasilinear-implicit differential equations (1996)
    Springer
    Computing 56 (1996), S. 47-59 
    Details
    ISSN: 1436-5057
    Keywords: 65 L 05 ; Rosenbrock-type methods ; quasilinear-implicit differential equations ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Bei der Lösung quasilinear-impliziter ODEs mittels Rosenbrock-Typ-Methoden können trotz guter Stabilitätseigenschaften (A- bzw. L-Stabilität) des Grundverfahrens Stabilitätsprobleme auftreten. Diese Schwierigkeiten sind auf Ungenauigkeiten bei der Berechnung künstlich eingeführter Komponenten (Überführung in DAEs) zurückzuführen. Die Arbeit untersucht die Ursachen für diese Effekte und zeigt Möglichkeiten, diese zu überwinden.
    Notes: Abstract The solution of quasilinear-implicit ODEs using Rosenbrock type methods may suffer from stability problems despite stability properties such as A-stability or L-stability, respectively. These problems are caused by inexact computation of artificial introduced components (transformation to DAE system). The paper investigates the source of the numerical difficulties and shows modifications to overcome them.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02238291
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  • 3
    Electronic Resource
    Electronic Resource
    Learning Game-Specific Spatially-Oriented Heuristics (1998)
    Springer
    Constraints 3 (1998), S. 239-253 
    Details
    ISSN: 1572-9354
    Keywords: machine learning ; game playing ; spatial cognition ; extensible architectures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes an architecture that begins with enough general knowledge to play any board game as a novice, and then shifts its decision-making emphasis to learned, game-specific, spatially-oriented heuristics. From its playing experience, it acquires game-specific knowledge about both patterns and spatial concepts. The latter are proceduralized as learned, spatially-oriented heuristics. These heuristics represent a new level of feature aggregation that effectively focuses the program's attention. While training against an external expert, the program integrates these heuristics robustly. After training it exhibits both a new emphasis on spatially-oriented play and the ability to respond to novel situations in a spatially-oriented manner. This significantly improves performance against a variety of opponents. In addition, we address the issue of context on pattern learning. The procedures described here move toward learning spatially-oriented heuristics for autonomous programs in other spatial domains.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009734028965
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  • 4
    Electronic Resource
    Electronic Resource
    Stability of non-Markovian polling systems (1995)
    Springer
    Queueing systems 21 (1995), S. 67-95 
    Details
    ISSN: 1572-9443
    Keywords: Polling systems ; stability ; stationary regime
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A stationary regime for polling systems with general ergodic (G/G) arrival processes at each station is constructed. Mutual independence of the arrival processes is not required. It is shown that the stationary workload so constructed is minimal in the stochastic ordering sense. In the model considered the server switches from station to station in a Markovian fashion, and a specific service policy is applied to each queue. Our hypotheses cover the purely gated, thea-limited, the binomial-gated and other policies. As a by-product we obtain sufficient conditions for the stationary regime of aG/G/1/∞ queue with multiple server vacations (see Doshi [11]) to be ergodic.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01158575
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  • 5
    Electronic Resource
    Electronic Resource
    Pathwise rate- stability for input-output processes (1996)
    Springer
    Queueing systems 22 (1996), S. 47-63 
    Details
    ISSN: 1572-9443
    Keywords: Sample-path analysis ; stability ; rate stability ; ω-rate stability ; input-output process ; queueing ; infinite-server queues
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract An input-output processZ = {Z(t), t ⩾ 0} is said to beω-rate stable ifZ(t) = o(ω(t)) for some non-negative functionω(t). We prove that the processZ is ω-rate stable under weak conditions that include the assumption that input satisfies a linear burstiness condition and Z is asymptotically average stable. In many cases of interest, the conditions forω-rate-stability can be verified from input data. For example, using input information, we establishω-rate stability of the workload for multiserver queues, an ATM multiplexer, andω-rate stability of queue-length processes for infinite server queues.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01159392
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  • 6
    Electronic Resource
    Electronic Resource
    On Whitt's conjecture for queues in which service times and interarrival times depend linearly and randomly upon waiting times (1996)
    Springer
    Queueing systems 22 (1996), S. 345-366 
    Details
    ISSN: 1572-9443
    Keywords: State-dependent service and interarrival times ; Lindley equation ; recursive stochastic equations ; stability ; normal approximations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider a modification of the standardG/G/1 queueing system with infinite waiting space and the first-in-first-out discipline in which the service times and interarrival times depend linearly and randomly on the waiting times. In this model the waiting times satisfy a modified version of the classical Lindley recursion. When the waiting-time distributions converge to a proper limit, Whitt [10] proposed a normal approximation for this steady-state limit. In this paper we prove a limit theorem for the steady-state limit of the system. Thus, our result provides a solid foundation for Whitt's normal approximation of the steady-state distribution of the system.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01149178
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  • 7
    Electronic Resource
    Electronic Resource
    On the stability of a partially accessible multi‐station queue with state‐dependent routing (1998)
    Springer
    Queueing systems 29 (1998), S. 55-73 
    Details
    ISSN: 1572-9443
    Keywords: multi‐server queue ; customer class ; state‐dependent routing ; stability ; Markov chain ; fluid limit
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider a multi‐station queue with a multi‐class input process when any station is available for the service of only some (not all) customer classes. Upon arrival, any customer may choose one of its accessible stations according to some state‐dependent policy. We obtain simple stability criteria for this model in two particular cases when service rates are either station‐ or class‐independent. Then, we study a two‐station queue under general assumptions on service rates. Our proofs are based on the fluid approximation approach.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019175812444
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  • 8
    Electronic Resource
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    A stability criterion via fluid limits and its application to a polling system (1999)
    Springer
    Queueing systems 32 (1999), S. 131-168 
    Details
    ISSN: 1572-9443
    Keywords: stability ; positive recurrence ; fluid limit ; polling system ; exhaustive service policy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a generalized criterion for the stability of Markovian queueing systems in terms of stochastic fluid limits. We consider an example in which this criterion may be applied: a polling system with two stations and two heterogeneous servers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019187004209
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  • 9
    Electronic Resource
    Electronic Resource
    Piecewise linear test functions for stability and instability of queueing networks (1997)
    Springer
    Queueing systems 27 (1997), S. 205-226 
    Details
    ISSN: 1572-9443
    Keywords: multiclass queueing networks ; ergodicity ; stability ; performance analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We develop the use of piecewise linear test functions for the analysis of stability of multiclass queueing networks and their associated fluid limit models. It is found that if an associated LP admits a positive solution, then a Lyapunov function exists. This implies that the fluid limit model is stable and hence that the network model is positive Harris recurrent with a finite polynomial moment. Also, it is found that if a particular LP admits a solution, then the network model is transient.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019166115653
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  • 10
    Electronic Resource
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    On the guaranteed throughput and efficiency of closed re-entrant lines (1998)
    Springer
    Queueing systems 28 (1998), S. 33-54 
    Details
    ISSN: 1572-9443
    Keywords: queueing networks ; throughput ; closed networks ; efficiency ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A closed network is said to be “guaranteed efficient” if the throughput converges under all non-idling policies to the capacity of the bottlenecks in the network, as the number of trapped customers increases to infinity. We obtain a necessary condition for guaranteed efficiency of closed re-entrant lines. For balanced two-station systems, this necessary condition is almost sufficient, differing from it only by the strictness of an inequality. This near characterization is obtained by studying a special type of virtual station called “alternating visit virtual station”. These special virtual stations allow us to relate the necessary condition to certain indices arising in heavy traffic studies using a Brownian network approximation, as well as to certain policies proposed as being extremal with respect to the asymptotic loss in the throughput. Using the near characterization of guaranteed efficiency we also answer the often pondered question of whether an open network or its closed counterpart has greater throughput - the answer is that neither can assure a greater guaranteed throughput.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019199022922
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  • 11
    Electronic Resource
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    Stability of a three‐station fluid network (1999)
    Springer
    Queueing systems 33 (1999), S. 293-325 
    Details
    ISSN: 1572-9443
    Keywords: stability ; fluid models ; multiclass queueing networks ; piecewise linear Lyapunov functions ; linear Lyapunov functions ; monotone global stability ; static buffer priority disciplines
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper studies the stability of a three‐station fluid network. We show that, unlike the two‐station networks in Dai and Vande Vate [18], the global stability region of our three‐station network is not the intersection of its stability regions under static buffer priority disciplines. Thus, the “worst” or extremal disciplines are not static buffer priority disciplines. We also prove that the global stability region of our three‐station network is not monotone in the service times and so, we may move a service time vector out of the global stability region by reducing the service time for a class. We introduce the monotone global stability region and show that a linear program (LP) related to a piecewise linear Lyapunov function characterizes this largest monotone subset of the global stability region for our three‐station network. We also show that the LP proposed by Bertsimas et al. [1] does not characterize either the global stability region or even the monotone global stability region of our three‐station network. Further, we demonstrate that the LP related to the linear Lyapunov function proposed by Chen and Zhang [11] does not characterize the stability region of our three‐station network under a static buffer priority discipline.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019184331042
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  • 12
    Electronic Resource
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    Automatically configuring constraint satisfaction programs: A case study (1996)
    Springer
    Constraints 1 (1996), S. 7-43 
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    ISSN: 1572-9354
    Keywords: constraint satisfaction algorithms ; machine learning ; configurable systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multi-tac is a learning system that synthesizes heuristic constraint satisfaction programs. The system takes a library of generic algorithms and heuristics and specializes them for a particular application. We present a detailed case study with three different distributions of a single combinatorial problem, “Minimum Maximal Matching”, and show that Muti-tac can synthesize programs for these different distributions that perform on par with hand-coded programs and that exceed the performance of some well-known satisfiability algorithms. In synthesizing a program, Multi-tac bases its choice of heuristics on an instance distribution, and we demonstrate that this capability has a significant impact on the results.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00143877
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  • 13
    Electronic Resource
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    Stability and performance analysis of rate-based feedback flow controlled ATM networks (1998)
    Springer
    Queueing systems 29 (1998), S. 129-159 
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    ISSN: 1572-9443
    Keywords: rate-based feedback control ; ATM networks ; stability ; optimal algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Motivated by ABR class of service in ATM networks, we study a continuous time queueing system with a feedback control of the arrival rate of some of the sources. The feedback about the queue length or the total workload is provided at regular intervals (variations on it, especially the traffic management specification TM 4.0, are also considered). The propagation delays can be nonnegligible. For a general class of feedback algorithms, we obtain the stability of the system in the presence of one or more bottleneck nodes in the virtual circuit. Our system is general enough that it can be useful to study feedback control in other network protocols. We also obtain rates of convergence to the stationary distributions and finiteness of moments. For the single botterneck case, we provide algorithms to compute the stationary distributions and the moments of the sojourn times in different sets of states. We also show analytically (by showing continuity of stationary distributions and moments) that for small propagation delays, we can provide feedback algorithms which have higher mean throughput, lower probability of overflow and lower delay jitter than any open loop policy. Finally these results are supplemented by some computational results.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019127929282
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  • 14
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    Dynamic scheduling in multiclass queueing networks: Stability under discrete-review policies (1999)
    Springer
    Queueing systems 31 (1999), S. 171-206 
    Details
    ISSN: 1572-9443
    Keywords: scheduling ; open multiclass queueing networks ; discrete-review policies ; fluid models ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a family of discrete-review policies for scheduling open multiclass queueing networks. Each of the policies in the family is derived from what we call a dynamic reward function: such a function associates with each queue length vector q and each job class k a positive value r k (q), which is treated as a reward rate for time devoted to processing class k jobs. Assuming that each station has a traffic intensity parameter less than one, all policies in the family considered are shown to be stable. In such a policy, system status is reviewed at discrete points in time, and at each such point the controller formulates a processing plan for the next review period, based on the queue length vector observed. Stability is proved by combining elementary large deviations theory with an analysis of an associated fluid control problem. These results are extended to systems with class dependent setup times as well as systems with alternate routing and admission control capabilities.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019106213778
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  • 15
    Electronic Resource
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    Dam processes with state dependent batch sizes and intermittent production processes with state dependent rates (1996)
    Springer
    Queueing systems 24 (1996), S. 37-57 
    Details
    ISSN: 1572-9443
    Keywords: dam ; storage process ; saturation rule ; intermittent production ; state dependent rates ; state dependent jumps ; stability ; positive Harris recurrence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider a dam process with a general (state dependent) release rule and a pure jump input process, where the jump sizes are state dependent. We give sufficient conditions under which the process has a stationary version in the case where the jump times and sizes are governed by a marked point process which is point (Palm) stationary and ergodic. We give special attention to the Markov and Markov regenerative cases for which the main stability condition is weakened. We then study an intermittent production process with state dependent rates. We provide sufficient conditions for stability for this process and show that if these conditions are satisfied, then an interesting new relationship exists between the stationary distribution of this process and a dam process of the type we explore here.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01149079
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  • 16
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    On the stability of retrial queues (1997)
    Springer
    Queueing systems 26 (1997), S. 343-363 
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    ISSN: 1572-9443
    Keywords: retrial queues ; stability ; ergodicity ; renovation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We consider the following Type of problems. Calls arrive at a queue of capacity K (which is called the primary queue), and attempt to get served by a single server. If upon arrival, the queue is full and the server is busy, the new arriving call moves into an infinite capacity orbit, from which it makes new attempts to reach the primary queue, until it finds it non-full (or it finds the server idle). If the queue is not full upon arrival, then the call (customer) waits in line, and will be served according to the FIFO order. If λ is the arrival rate (average number per time unit) of calls and μ is one over the expected service time in the facility, it is well known that μ 〉 λ is not always sufficient for stability. The aim of this paper is to provide general conditions under which it is a sufficient condition. In particular, (i) we derive conditions for Harris ergodicity and obtain bounds for the rate of convergence to the steady state and large deviations results, in the case that the inter-arrival times, retrial times and service times are independent i.i.d. sequences and the retrial times are exponentially distributed; (ii) we establish conditions for strong coupling convergence to a stationary regime when either service times are general stationary ergodic (no independence assumption), and inter-arrival and retrial times are i.i.d. exponentially distributed; or when inter-arrival times are general stationary ergodic, and service and retrial times are i.i.d. exponentially distributed; (iii) we obtain conditions for the existence of uniform exponential bounds of the queue length process under some rather broad conditions on the retrial process. We finally present conditions for boundedness in distribution for the case of nonpatient (or non persistent) customers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019193527040
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  • 17
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    Recurrence and transience properties of some neural networks: an approach via fluid limit models (1999)
    Springer
    Queueing systems 32 (1999), S. 99-130 
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    ISSN: 1572-9443
    Keywords: neural network ; inhibition ; stability ; Markov process ; fluid limit ; Harris-recurrence ; transience
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The subject of the paper is the stability analysis of some neural networks consisting of a finite number of interacting neurons. Following the approach of Dai [5] we use the fluid limit model of the network to derive a sufficient condition for positive Harris-recurrence of the associated Markov process. This improves the main result in Karpelevich et al. [11] and, at the same time, sheds some new light on it. We further derive two different conditions that are sufficient for transience of the state process and illustrate our results by classifying some examples according to positive recurrence or transience.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019135020138
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  • 18
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    Window flow control in FIFO networks with cross traffic (1999)
    Springer
    Queueing systems 32 (1999), S. 195-231 
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    ISSN: 1572-9443
    Keywords: window flow control ; TCP ; stability ; multiclass networks ; stationary ergodic point processes ; (max,+)-linear system
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We focus on window flow control as used in packet-switched communication networks. The approach consists in studying the stability of a system where each node on the path followed by the packets of the controlled connection is modeled by a FIFO (First-In-First-Out) queue of infinite capacity which receives in addition some cross traffic represented by an exogenous flow. Under general stochastic assumptions, namely for stationary and ergodic input processes, we show the existence of a maximum throughput allowed by the flow control. Then we establish bounds on the value of this maximum throughput. These bounds, which do not coincide in general, are reached by time-space scalings of the exogenous flows. Therefore, the performance of the window flow control depends not only on the traffic intensity of the cross flows, but also on fine statistical characteristics such as the burstiness of these flows. These results are illustrated by several examples, including the case of a nonmonotone, nonconvex and fractal stability region.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1019191105117
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  • 19
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    Real-World Robotics: Learning to Plan for Robust Execution (1996)
    Springer
    Machine learning 23 (1996), S. 121-161 
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    ISSN: 0885-6125
    Keywords: machine learning ; robotics ; uncertainty ; planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
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    URL: http://dx.doi.org/10.1023/A:1018274104280
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  • 20
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    First Order Regression (1997)
    Springer
    Machine learning 26 (1997), S. 147-176 
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    ISSN: 0885-6125
    Keywords: machine learning ; inductive logic programming ; regression ; real-valued variables ; first-order logic ; applications of machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of FORS on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of FORS' performance with previous results in these domains indicates that FORS is an effective tool for ILP applications that involve numerical data.
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    URL: http://dx.doi.org/10.1023/A:1007365207130
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  • 21
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    PLATA: An Application of LEGAL, a Machine Learning Based System, to a Typology of Archaeological Ceramics (1997)
    Springer
    Computers and the humanities 31 (1997), S. 169-187 
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    ISSN: 1572-8412
    Keywords: archaeological typology ; ceramics ; knowledge acquisition ; machine learning ; Sudan
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Notes: Abstract The authors here show that machine learning techniques can be used for designing an archaeological typology, at an early stage when the classes are not yet well defined. The program (LEGAL, LEarning with GAlois Lattice) is a machine learning system which uses a set of examples and counter-examples in order to discriminate between classes. Results show a good compatibility between the classes such as the yare defined by the system and the archaeological hypotheses.
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    URL: http://dx.doi.org/10.1023/A:1000904004065
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  • 22
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    The Frame Problem: An AI Fairy Tale (1998)
    Springer
    Minds and machines 8 (1998), S. 317-351 
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    ISSN: 1572-8641
    Keywords: artificial intelligence ; frame problem ; causal induction ; machine learning ; logicism ; Bayesian learning ; MML
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers.
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    URL: http://dx.doi.org/10.1023/A:1008286921835
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  • 23
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    How do Simple Rules `Fit to Reality' in a Complex World? (1999)
    Springer
    Minds and machines 9 (1999), S. 543-564 
    Details
    ISSN: 1572-8641
    Keywords: Bayesianism ; complexity ; decision theory ; fast and frugal heuristics ; machine learning ; philosophy of science ; predictive accuracy ; simplicity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science.
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    URL: http://dx.doi.org/10.1023/A:1008304819398
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    Machine learning applications in anthropology: Automated discovery over kinship structures (1996)
    Springer
    Computers and the humanities 30 (1996), S. 401-406 
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    ISSN: 1572-8412
    Keywords: machine learning ; induction ; inductive logic programming ; FOIL
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Media Resources and Communication Sciences, Journalism
    Notes: Abstract A common problem in anthropological field work is generalizing rules governing social interactions and relations (particularly kinship) from a series of examples. One class of machine learning algorithms is particularly well-suited to this task: inductive logic programming systems, as exemplified by FOIL. A knowledge base of relationships among individuals is established, in the form of a series of single-predicate facts. Given a set of positive and negative examples of a new relationship, the machine learning programs build a Horn clause description of the target relationship. The power of these algorithms to derive complex hypotheses is demonstrated for a set of kinship relationships drawn from the anthropological literature. FOIL extends the capabilities of earlier anthropology-specific learning programs by providing a more powerful representation for induced relationships, and is better able to learn in the face of noisy or incomplete data.
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    URL: http://dx.doi.org/10.1007/BF00057936
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    The finite volume element method with quadratic basis functions (1996)
    Springer
    Computing 57 (1996), S. 281-299 
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    ISSN: 1436-5057
    Keywords: 65N15 ; 65N99 ; 35A40 ; Finite volume method ; box scheme ; stability ; error estimates
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Es wird eine Box-Methode mit quadratischen Ansatzfunktionen zur Diskretisierung elliptischer Randwertaufgaben vorgestellt. Die entstehende Diskretisierungsmatrix ist nichsymmetrisch. Die Stabilitätsanalyse basiert auf einer elementweisen Abschätzung des Skalarproduktes 〈A h u h ,u h 〉. Hinreichende Bedingungen an die Geometrie der Dreiecke der Triangulierung führen zur diskreten Elliptizität. Unter diesen Voraussetzungen wird eineO(h 2)-Fehlerabschätzung bewiesen.
    Notes: Abstract The paper presents a box scheme with quadratic basis functions for the discretisation of elliptic boundary value problems. The resulting discretisation matrix is non-symmetrical (and also not an M-matrix). The stability analysis is based on an elementwise estimation of the scalar product 〈A h u h ,u h 〉. Sufficient conditions placed on the triangles of the triangulation lead to discrete ellipticity. Proof of anO(h 2) error estimate is given for these conditions.
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    Difference schemes for the dispersive equation (1983)
    Springer
    Computing 31 (1983), S. 261-267 
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    ISSN: 1436-5057
    Keywords: 65M10 ; Dispersive equation ; finite difference ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Dieser Artikel beinhaltet eine Zusammenstellung von Differenzenverfahren für die Dispersionsgleichungu 1=au xxx. Es werden Kriterien zur Herleitung von Stabilitätsbedingungen für Differenzenverfahren angegeben und auf die angegebenen Differenzenverfahren angewendet.
    Notes: Abstract In this paper a table of difference schemes for the dispersive equationu i=au xxx is presented. A collection of criterions for deriving stability conditions of difference schemes is given and applied to these difference schemes.
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    URL: http://dx.doi.org/10.1007/BF02263436
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    D-stability and Kaps-Rentrop-methods (1984)
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    Computing 32 (1984), S. 229-237 
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    ISSN: 1436-5057
    Keywords: 65L05 ; 65L07 ; Stiff system ; Rosenbroek method ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung In dieser Arbeit wird die Stabilität des Kaps-Rentrop-Verfahrens in die Anwesenheit nichtlinearer Steifheit (Stiffness) analysiert. Dazu werden mittels eines einfachen Modells zwei Größen introduziert. Die Werte dieser Größen reflektieren gewissermaßen das Verhalten eines Kaps-Rentrop-Verfahrens in die Anwesenheit einer bestimmten Kopplung zwischen die beiden Komponenten in das steife System gewöhnlicher Differentialgleichungen. Einige numerische Beispiele veranschaulichen die Analyse.
    Notes: Abstract In this paper we give an analysis of the effect of stiff nonlinearities on the behavior of a Kaps-Rentrop method. To that end we introduce two quantities related to a simple model. The values of these quantities determine to some extent the behavior of a Kaps-Rentrop method in case of a strong coupling between the smooth component and the transient one. Numerical examples illustrate the theoretical results.
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    Named-Entity Recognition from Greek and English Texts (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 123-135 
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    ISSN: 1573-0409
    Keywords: named-entity recognition ; information extraction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
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    URL: http://dx.doi.org/10.1023/A:1008124406923
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    Application of Machine Learning in Water Distribution Networks Assisted by Domain Experts (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 325-352 
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    ISSN: 1573-0409
    Keywords: machine learning ; water distribution network ; knowledge acquisition ; forecasting ; exception handling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The human-assisted application of machine learning techniques in the domain of water distribution networks is presented, corresponding to a research work done in the context of the European Esprit project WATERNET. One part of this project is a learning system that intends to capture knowledge from historic information collected during the operation of water distribution networks. The captured knowledge is expected to contribute to the improvement of the operation of the network. Presented ideas correspond to the first development phase of the learning system, focusing specially on the adopted methodology. The interactions between different classes of human experts and the learning system are also discussed. Finally some experimental results are presented.
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    URL: http://dx.doi.org/10.1023/A:1008193214890
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    Autonomous Intelligent Cruise Control with Actuator Delays (1998)
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 27-43 
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    ISSN: 1573-0409
    Keywords: autonomous control ; actuator delays ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this paper, we consider the control design problem of vehicle following systems with actuator delays. An upper bound for the time delays is first constructed to guarantee the vehicle stability. Second, sufficient conditions are presented to avoid slinky-effects in the vehicle following. Next, zero steady state achieved by the proposed controller is proven. Finally, simulations are given to examine our claims.
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    Extracting knowledge from examples: Induction of heuristic rules for wheelchair prescription (1995)
    Springer
    Journal of intelligent and robotic systems 14 (1995), S. 133-153 
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    ISSN: 1573-0409
    Keywords: Wheelchair prescription ; ID3 ; machine learning ; expert system ; rehabilitation ; equipment selection ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The feasibility of using an induction algorithm to discover heuristic rules for wheelchair equipment selection is investigated. Syntactical rules for two description languages (one to describe the disabled client and another to describe wheelchair equipment configurations) are presented. These languages allow the rulebase developer to describe training instances (examples) to the computer in an intelligible way. An induction learning algorithm is used to classify these training instances, thereby producing a decision tree. Heuristic rules can then be extracted from the tree and placed in a rulebase for an expert system called LEADER. LEADER is being developed to aid a wheelchair prescriber in the equipment selection process. There are two important objectives of this research: (1) to reduce the time and facilitate the development of an intelligent expert system rulebase by extracting knowledge embedded within existing examples and (2) to provide the expert system with the ability to learn new rules autonomously. The ability to learn makes the rulebase dynamic; the initial rulebase would be augmented with new rules as additional examples are provided to the system while it is in clinical use.
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    Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks (1997)
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 131-155 
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    ISSN: 1573-0409
    Keywords: robot adaptive control ; basis function-like networks ; stability ; discrete variable structure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Stable neural network-based sampled-data indirect and direct adaptivecontrol approaches, which are the integration of a neural network (NN)approach and the adaptive implementation of the discrete variable structurecontrol, are developed in this paper for the trajectory tracking control ofa robot arm with unknown nonlinear dynamics. The robot arm is assumed tohave an upper and lower bound of its inertia matrix norm and its states areavailable for measurement. The discrete variable structure control servestwo purposes, i.e., one is to force the system states to be within the stateregion in which neural networks are used when the system goes out of neuralcontrol; and the other is to improve the tracking performance within the NNapproximation region. Main theory results for designing stable neuralnetwork-based sampled data indirect and direct adaptive controllers aregiven, and the extension of the proposed control approaches to the compositeadaptive control of a flexible-link robot is discussed. Finally, theeffectiveness of the proposed control approaches is illustrated throughsimulation studies.
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    Robust Regulator for Flexible-Joint Robots Using Integrator Backstepping (1998)
    Springer
    Journal of intelligent and robotic systems 22 (1998), S. 23-38 
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    ISSN: 1573-0409
    Keywords: robot dynamic model ; stiffness matrix ; constant disturbance ; integrator backstepping ; Liapunov functions ; Barbalat lemma ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A robust regulator for flexible-joint robots is proposed, which yields constant torque disturbance rejection acting on the links. The design uses the integrator backstepping technique [4,5] to cancel nonlinearities and disturbance not in the range space of the control. Stability of the closed loop system is shown using iterative Liapunov functions.
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    Forward Assembly Planning Based on Stability (1997)
    Springer
    Journal of intelligent and robotic systems 19 (1997), S. 411-436 
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    ISSN: 1573-0409
    Keywords: assembly planning ; stability ; robot ; forward ; operations
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The paper presents an approach to sequence planning consisting in determining assembly sequences defined in terms of mating and non-mating operations and based on a dynamic expansion of the assembly tree obtained using a knowledge base management system. The planner considers the case of a single-robot assembly workcell. The use of stability and the detailed definition of sequences also by means of several non-mating operations are shown to be powerful instruments in the control of the tree expansion. Forward assembly planning has been chosen, in order to minimize the number of stability checks. Backtracking is avoided by combining precedence relations and stability analysis. Hard and soft constrains are introduced to drive the tree expansion. Hard constraints are precedence relations and stability analysis. All operations are associated to costs, which are used as soft constraints. The operation based approach enables one to manage even non-mating operations and to easily overcome the linearity constraint. Costs enable the planner to manage the association among tools and components. The first section of the paper concerns Stability Analysis that is subdivided into Static and Dynamic Stability Analysis. The former is mainly involved in analyzing gravity effects; the latter is mainly involved in evaluate inertia effects due to manipulation. Stability Analysis is implemented in a simplified form. Fundamental assumptions are: no rotational equilibrium condition is considered; for each reaction force only direction and versus, but not magnitude, are considered; friction is neglected. The second section discusses the structure of the planner and its implementation. The planner is a rule based system. Forward chaining and hypothetical reasoning are the inference strategies used. The knowledge base and the data base of the system are presented and the advantages obtained using a rule based system are discussed. The third section shows two planning examples, showing the performance of the system in a simple case and in an industrial test case, the assembly of a microwave branching filter composed of 26 components.
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    Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 91-100 
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    ISSN: 1573-0409
    Keywords: robots ; neural networks ; adaptiveness ; stability ; approximation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies.
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    Cognitive Architecture for Robust Adaptive Control of Robots in a Team (1997)
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 251-273 
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    ISSN: 1573-0409
    Keywords: robot control ; adaptive behavior ; robust intelligent control ; multi-robot systems ; machine learning ; neural networks ; genetic algorithms ; cognitive architecture.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.
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    Multi-Layer Hierarchical Rule Learning in Reactive Robot Control Using Incremental Decision Trees (1999)
    Springer
    Journal of intelligent and robotic systems 24 (1999), S. 99-124 
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    ISSN: 1573-0409
    Keywords: behaviour decomposition ; behaviour learning ; intelligent navigation ; decision tress ; ITI ; machine learning ; robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper presents a new approach to the intelligent navigation of a mobile robot. The hybrid control architecture described combines properties of purely reactive and behaviour-based systems, providing the ability both to learn automatically behaviours from inception, and to capture these in a distributed hierarchy of decision tree networks. The robot is first trained in the simplest world which has no obstacles, and is then trained in successively more complex worlds, using the knowledge acquired in the previous worlds. Each world representing the perceptual space is thus directly mapped on a unique rule layer which represents in turn the robot action space encoded in a distinct decision tree. A major advantage of the current implementation, compared with the previous work, is that the generated rules are easily understood by human users. The paper demonstrates that the proposed behavioural decomposition approach provides efficient management of complex knowledge, and that the learning mechanism is able to cope with noise and uncertainty in sensory data.
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    Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition (1995)
    Springer
    Machine learning 18 (1995), S. 109-114 
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    ISSN: 0885-6125
    Keywords: Expert systems ; machine learning ; explicit vs ; implicit knowledge acquisition ; classification accuracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.
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    Classification accuracy: Machine learning vs. explicit knowledge acquisition (1995)
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    Machine learning 18 (1995), S. 109-114 
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    ISSN: 0885-6125
    Keywords: Expert systems ; machine learning ; explicit vs. implicit knowledge acquisition ; classification accuracy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.
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    Learning from a Population of Hypotheses (1995)
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    Machine learning 18 (1995), S. 255-276 
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    ISSN: 0885-6125
    Keywords: machine learning ; computational learning theory ; PAC learning ; learning agents
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.
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    Learning from a population of hypotheses (1995)
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    Machine learning 18 (1995), S. 255-276 
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    ISSN: 0885-6125
    Keywords: machine learning ; computational learning theory ; PAC learning ; learning agents
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.
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    Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers (1998)
    Springer
    Machine learning 31 (1998), S. 115-139 
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    ISSN: 0885-6125
    Keywords: neural network controllers ; machine learning ; innateness ; biologically inspired robotics ; quantification in robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The aim was to investigate a method of developing mobile robot controllers based on ideas about how plastic neural systems adapt to their environment by extracting regularities from the amalgamated behavior of inflexible (non-plastic) innate s ubsystems interacting with the world.Incremental bootstrapping of neural network controllers was examined. The objective was twofold. First, to develop and evaluate the use of prewired or innate robot controllers to bootstrap backpropagation learning for Multi-Layer Perceptron (MLP) controllers. Second, to develop and evaluate a new MLP controller trained on the back of another bootstrapped controller. The experimental hypothesis was that MLPs would improve on the performance of controllers used to train them. The performances of the innate and bootstrapped MLP controllers were compared in eight experiments on the tasks of avoiding obstacles and finding goals. Four quantitative measures were employed: the number of sensorimotor loops required to complete a task; the distance traveled; the mean distance from walls and obstacles; the smoothness of travel. The overall pattern of results from statistical analyses of these quantities su pported the hypothesis; the MLP controllers completed the tasks faster, smoother, and steered further from obstacles and walls than their innate teachers. In particular, a single MLP controller incrementally bootstrapped by a MLP subsumption controller was superior to the others.
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    Learning to Recognize Volcanoes on Venus (1998)
    Springer
    Machine learning 30 (1998), S. 165-194 
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    ISSN: 0885-6125
    Keywords: machine learning ; pattern recognition ; learning from examples ; large image databases ; data mining ; automatic cataloging ; detection of natural objects ; Magellan SAR ; JARtool ; volcanoes ; Venus ; principal components analysis ; trainable
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to “toy world” domains. This paper discusses important aspects of the application process not commonly encountered in the “toy world,” including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.
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    Technical note: Bias and the quantification of stability (1995)
    Springer
    Machine learning 20 (1995), S. 23-33 
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    ISSN: 0885-6125
    Keywords: stability ; bias ; accuracy ; repeatability ; agreement ; similarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.
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    Real-world robotics: Learning to plan for robust execution (1996)
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    Machine learning 23 (1996), S. 121-161 
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    ISSN: 0885-6125
    Keywords: machine learning ; robotics ; uncertainty ; planning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, “proves” that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.
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    On the worst-case analysis of temporal-difference learning algorithms (1996)
    Springer
    Machine learning 22 (1996), S. 95-121 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; temporal-difference learning ; on-line learning ; worst-case analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study the behavior of a family of learning algorithms based on Sutton's method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, and the goal of the learning algorithm is to estimate a discounted sum of all the reinforcements that will be received in the future. In this setting, we are able to prove general upper bounds on the performance of a slightly modified version of Sutton's so-called TD(λ) algorithm. These bounds are stated in terms of the performance of the best linear predictor on the given training sequence, and are proved without making any statistical assumptions of any kind about the process producing the learner's observed training sequence. We also prove lower bounds on the performance of any algorithm for this learning problem, and give a similar analysis of the closely related problem of learning to predict in a model in which the learner must produce predictions for a whole batch of observations before receiving reinforcement.
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    Technical Note: Bias and the Quantification of Stability (1995)
    Springer
    Machine learning 20 (1995), S. 23-33 
    Details
    ISSN: 0885-6125
    Keywords: stability ; bias ; accuracy ; repeatability ; agreement ; similarity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.
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    On the Worst-Case Analysis of Temporal-Difference Learning Algorithms (1996)
    Springer
    Machine learning 22 (1996), S. 95-121 
    Details
    ISSN: 0885-6125
    Keywords: machine learning ; temporal-difference learning ; on-line learning ; worst-case analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study the behavior of a family of learning algorithms based on Sutton‘s method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, and the goal of the learning algorithm is to estimate a discounted sum of all the reinforcements that will be received in the future. In this setting, we are able to prove general upper bounds on the performance of a slightly modified version of Sutton‘s so-called TD((gl) algorithm. These bounds are stated in terms of the performance of the best linear predictor on the given training sequence, and are proved without making any statistical assumptions of any kind about the process producing the learner‘s observed training sequence. We also prove lower bounds on the performance of any algorithm for this learning problem, and give a similar analysis of the closely related problem of learning to predict in a model in which the learner must produce predictions for a whole batch of observations before receiving reinforcement.
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    Emergence of symmetric, modular, and reciprocal connections in recurrent networks with Hebbian learning (1999)
    Springer
    Biological cybernetics 81 (1999), S. 211-225 
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    ISSN: 1432-0770
    Keywords: Key words: Hebbian learning rule ; attractor dynamics ; symmetric connections ; multiplicative normalization ; self-organization ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. While learning and development are well characterized in feedforward networks, these features are more difficult to analyze in recurrent networks due to the increased complexity of dual dynamics – the rapid dynamics arising from activation states and the slow dynamics arising from learning or developmental plasticity. We present analytical and numerical results that consider dual dynamics in a recurrent network undergoing Hebbian learning with either constant weight decay or weight normalization. Starting from initially random connections, the recurrent network develops symmetric or near-symmetric connections through Hebbian learning. Reciprocity and modularity arise naturally through correlations in the activation states. Additionally, weight normalization may be better than constant weight decay for the development of multiple attractor states that allow a diverse representation of the inputs. These results suggest a natural mechanism by which synaptic plasticity in recurrent networks such as cortical and brainstem premotor circuits could enhance neural computation and the generation of motor programs.
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    Stability analysis of a general toeplitz systems solver (1995)
    Springer
    Numerical algorithms 10 (1995), S. 225-244 
    Details
    ISSN: 1572-9265
    Keywords: Cholesky factorization error analysis ; Hankel matrix ; least squares ; normal equations ; orthogonal factorization ; QR factorization ; semi-normal equations ; stability ; Toeplitz matrix ; weak stability ; Primary 65F25 ; Secondary 47B35 ; 65F05 ; 65F30 ; 65Y05 ; 65Y10
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We show that a fast algorithm for theQR factorization of a Toeplitz or Hankel matrixA is weakly stable in the sense thatR T R is close toA T A. Thus, when the algorithm is used to solve the semi-normal equationsR TRx=AT b, we obtain a weakly stable method for the solution of a nonsingular Toeplitz or Hankel linear systemAx=b. The algorithm also applies to the solution of the full-rank Toeplitz or Hankel least squares problem min ||Ax-b||2.
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    Progressive stable interpolation (1997)
    Springer
    Numerical algorithms 14 (1997), S. 343-359 
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    ISSN: 1572-9265
    Keywords: progressive interpolation ; stability ; spline ; shape parameters ; geometric continuity ; 41A05 ; 41A15 ; 65D05 ; 65D07
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper, we study several interpolating and smoothing methods for data which are known “progressively”. The algorithms proposed are governed by recurrence relations and our principal goal is to study their stability. A recurrence relation will be said stable if the spectral radius of the associated matrix is less than one. The iteration matrices depend on shape parameters which come either from the connection at the knots, or from the nature of the interpolant between two knots. We obtain various stability domains. Moving the parameters inside these domains leads to interesting shape effects.
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    Multistep methods for differential algebraic equations (1995)
    Springer
    Numerical algorithms 10 (1995), S. 245-260 
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    ISSN: 1572-9265
    Keywords: Multistep methods ; differential-algebraic equations ; stability ; existence and uniqueness ; convergence of iterative method ; 65L06 ; 65L20 ; 65N22
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Multistep methods for the differential/algebraic equations (DAEs) in the form of $$F_1 (x) = 0, F_2 (x,x',z) = 0$$ are presented, whereF 1 maps from ℝ n to ℝ ′ ,F 2 from ℝ n x ℝ n x ℝ m to ℝ s andr〈n≤r+s=n+m. By employing the deviations of the available existence theories, a new form of the multistep method for solutions of (1) is developed. Furthermore, it is shown that this method has no typical instabilities such as those that may occur in the application of multistep method to DAEs in the traditional manner. A proof of the solvability of the multistep system is provided, and an iterative method is developed for solving these nonlinear algebraic equations. Moreover, a proof of the convergence of this iterative method is presented.
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    Learning, goals, and learning goals: A perspective on goal-driven learning (1995)
    Springer
    Artificial intelligence review 9 (1995), S. 387-422 
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    ISSN: 1573-7462
    Keywords: machine learning ; cognitive modeling ; metacognition ; active learning ; multistrategy learning ; utility of learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet ofgoal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This article examines the motivations for adopting a goal-driven model of learning, the relationship between task goals and learning goals, the influences goals can have on learning, and the pragmatic implications of the goal-driven learning model. It presents a new integrative framework for understanding the goal-driven learning process and applies this framework to characterizing research on goal-driven learning.
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    An Adaptable IE System to New Domains (1999)
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    Applied intelligence 10 (1999), S. 225-246 
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    ISSN: 1573-7497
    Keywords: information extraction ; automatic pattern acquisition ; machine learning ; EuroWordNet
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The most extended way of acquiring information for knowledge based systems is to do it manually. However, the high cost of this approach and the availability of alternative Knowledge Sources has lead to an increasing use of automatic acquisition approaches. In this paper we present M-TURBIO, a Text-Based Intelligent System (TBIS) that extracts information contained in restricted-domain documents. The system acquires part of its knowledge about the structure of the documents and the way the information is presented (i.e., syntactic-semantic rules) from a training set of these. Then, a database is created by means of applying these syntactic-semantic rules to extract the information contained in the whole document.
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    Comparing Structures Using a Hopfield-Style Neural Network (1999)
    Springer
    Applied intelligence 11 (1999), S. 15-30 
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    ISSN: 1573-7497
    Keywords: neural networks ; structured objects ; machine learning ; classification ; similarity ; nearest neighbor
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Labeled graphs are an appropriate and popular representation of structured objects in many domains. If the labels describe the properties of real world objects and their relations, finding the best match between two graphs turns out to be the weakly defined, NP-complete task of establishing a mapping between them that maps similar parts onto each other preserving as much as possible of their overall structural correspondence. In this paper, former approaches of structural matching and constraint relaxation by spreading activation in neural networks and the method of solving optimization tasks using Hopfield-style nets are combined. The approximate matching task is reformulated as the minimization of a quadratic energy function. The design of the approach enables the user to change the parameters and the dynamics of the net so that knowledge about matching preferences is included easily and transparently. In the last section, some examples demonstrate the successful application of this approach in classification and learning in the domain of organic chemistry.
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    Imputation of Missing Data in Industrial Databases (1999)
    Springer
    Applied intelligence 11 (1999), S. 259-275 
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    ISSN: 1573-7497
    Keywords: missing data ; industrial databases ; multiple imputation ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.
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    Exploiting Hierarchy in Text Categorization (1999)
    Springer
    Information retrieval 1 (1999), S. 193-216 
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    ISSN: 1573-7659
    Keywords: information retrieval ; text mining ; topic spotting ; text categorization ; knowledge management ; problem decomposition ; machine learning ; neural networks ; probabilistic models ; hierarchical models ; performance evaluation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract With the recent dramatic increase in electronic access to documents, text categorization—the task of assigning topics to a given document—has moved to the center of the information sciences and knowledge management. This article uses the structure that is present in the semantic space of topics in order to improve performance in text categorization: according to their meaning, topics can be grouped together into “meta-topics”, e.g., gold, silver, and copper are all metals. The proposed architecture matches the hierarchical structure of the topic space, as opposed to a flat model that ignores the structure. It accommodates both single and multiple topic assignments for each document. Its probabilistic interpretation allows its predictions to be combined in a principled way with information from other sources. The first level of the architecture predicts the probabilities of the meta-topic groups. This allows the individual models for each topic on the second level to focus on finer discriminations within the group. Evaluating the performance of a two-level implementation on the Reuters-22173 testbed of newswire articles shows the most significant improvement for rare classes.
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    Discovering dynamics: From inductive logic programming to machine discovery (1995)
    Springer
    Journal of intelligent information systems 4 (1995), S. 89-108 
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    ISSN: 1573-7675
    Keywords: machine discovery ; machine learning ; dynamical system identification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Machine discovery systems help humans to find natural laws from collections of experimentally collected data. Most of the laws found by existing machine discovery systems describe static situations, where a physical system has reached equilibrium. In this paper, we consider the problem of discovering laws that govern the behavior of dynamical systems, i.e., systems that change their state over time. Based on ideas from inductive logic programming and machine discovery, we present two systems, QMN and LAGRANGE, for discovery of qualitative and quantitative laws from quantitative (numerical) descriptions of dynamical system behavior. We illustrate their use by generating a variety of dynamical system models from example behaviors.
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    Induction of ripple-down rules applied to modeling large databases (1995)
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    Journal of intelligent information systems 5 (1995), S. 211-228 
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    ISSN: 1573-7675
    Keywords: inductive database modeling ; induction ; machine learning ; medical diagnosis ; ripple-down rules ; rules with exceptions ; Induct ; Garvan thyroid database
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A methodology forthe modeling of large data sets is described which results in rule sets having minimal inter-rule interactions, and being simply maintained. An algorithm for developing such rule sets automatically is described and its efficacy shown with standard test data sets. Comparative studies of manual and automatic modeling of a data set of some nine thousand five hundred cases are reported. A study is reported in which ten years of patient data have been modeled on a month by month basis to determine how well a diagnostic system developed by automated induction would have performed had it been in use throughout the project.
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    A Bayesian method for learning belief networks that contain hidden variables (1995)
    Springer
    Journal of intelligent information systems 4 (1995), S. 71-88 
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    ISSN: 1573-7675
    Keywords: probabilistic networks ; Bayesian belief networks ; hidden variables ; machine learning ; induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents a Bayesian method for computing the probability of a Bayesian belief-network structure from a database. In particular, the paper focuses on computing the probability of a belief-network structure that contains a hidden (latent) variable. A hidden variable represents a postulated entity that has not been directly measured. After reviewing related techniques, which previously were reported, this paper presents a new, more efficient method for handling hidden variables in belief networks.
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    Modified Conjugate Gradient Method for the Solution of Ax=b (1998)
    Springer
    Journal of scientific computing 13 (1998), S. 173-183 
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    ISSN: 1573-7691
    Keywords: Modified conjugate gradient method ; conjugate gradient method ; Krylov space ; convergence rate ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this note, we examine a modified conjugate gradient procedure for solving $$A\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{x} = \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ in which the approximation space is based upon the Krylov space ( $$A\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{x} = \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ ) associated with $$\sqrt A$$ and $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ . We show that, given initial vectors $$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ and $$\sqrt A \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ (possibly computed at some expense), the best fit solution in $$K^k \sqrt A ,\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{b}$$ can be computed using a finite-term recurrence requiring only one multiplication by A per iteration. The initial convergence rate appears, as expected, to be twice as fast as that of the standard conjugate gradient method, but stability problems cause the convergence to be degraded.
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    A New ADI Scheme for Solving Three-Dimensional Parabolic Differential Equations (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 361-369 
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    ISSN: 1573-7691
    Keywords: Alternating-direction implicit ; difference scheme ; stability ; convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new alternating-direction implicit (ADI) scheme for solving three-dimensional parabolic differential equations has been developed based on the idea of regularized difference scheme. It is unconditionally stable and second-order accurate. Further, it overcomes the drawback of the Douglas scheme and is to be very well to simulate fast transient phenomena and to efficiently capture steady state solutions of parabolic differential equations. Numerical example is illustrated.
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    Stability of traffic patterns in broadband networks (1995)
    Springer
    Journal of network and systems management 3 (1995), S. 371-380 
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    ISSN: 1573-7705
    Keywords: Telephone traffic ; network management ; control theory ; dynamic flows ; stability ; routing algorithms ; broadband networks ; simulation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The control of telephony traffic is the task of network management and routing algorithms. In this paper, a study of two trunk groups carrying telephony traffic is used to show that instabilities can arise if there is a delay in getting feedback information for a network controller. The network controller seeks to balance the traffic in the two trunk groups, which may represent two paths from a source to a destination. An analysis shows how factors such as holding time, controller gain and feedback delay influence stability. Simulation of a two service case is also carried out to show that the same instabilities can arise.
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    Convergence of Delayed Dynamical Systems (1999)
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    Neural processing letters 10 (1999), S. 267-271 
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    ISSN: 1573-773X
    Keywords: recurrent neural networks ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, we point out that the conditions given in [1] are sufficient but unnecessary for the global asymptotically stable equilibrium of a class of delay differential equations. Instead, we prove that under weaker conditions, it is still global asymptotically stable.
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    The Connectionist Inductive Learning and Logic Programming System (1999)
    Springer
    Applied intelligence 11 (1999), S. 59-77 
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    ISSN: 1573-7497
    Keywords: theory refinement ; machine learning ; artificial neural networks ; logic programming ; computational biology
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL2P). C-IL2P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Programming. Starting with the background knowledge represented by a propositional logic program, a translation algorithm is applied generating a neural network that can be trained with examples. The results obtained with this refined network can be explained by extracting a revised logic program from it. Moreover, the neural network computes the stable model of the logic program inserted in it as background knowledge, or learned with the examples, thus functioning as a parallel system for Logic Programming. We have successfully applied C-IL2P to two real-world problems of computational biology, specifically DNA sequence analyses. Comparisons with the results obtained by some of the main neural, symbolic, and hybrid inductive learning systems, using the same domain knowledge, show the effectiveness of C-IL2P.
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    Knowledge Discovery in an Oceanographic Database (1999)
    Springer
    Applied intelligence 11 (1999), S. 135-148 
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    ISSN: 1573-7497
    Keywords: knowledge discovery ; machine learning ; texture ; feature selection ; image processing ; clusturing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Knowledge discovery from image data is a multi-step iterative process. This paper describes the procedure we have used to develop a knowledge discovery system that classifies regions of the ocean floor based on textural features extracted from acoustic imagery. The image is subdivided into rectangular cells called texture elements (texels); a gray-level co-occurence matrix (GLCM) is computed for each texel in four directions. Secondary texture features are then computed from the GLCM resulting in a feature vector representation of each texel instance. Alternatively, a region-growing approach is used to identify irregularly shaped regions of varying size which have a homogenous texture and for which the texture features are computed. The Bayesian classifier Autoclass is used to cluster the instances. Feature extraction is one of the major tasks in knowledge discovery from images. The initial goal of this research was to identify regions of the image characterized by sand waves. Experiments were designed to use expert judgements to select the most effective set of features, to identify the best texel size, and to determine the number of meaningful classes in the data. The region-growing approach has proven to be more successful than the texel-based approach. This method provides a fast and accurate method for identifying provinces in the ocean floor of interest to geologists.
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    Time separation technique for large databases (1983)
    Springer
    International journal of parallel programming 12 (1983), S. 193-209 
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    ISSN: 1573-7640
    Keywords: Database ; characteristic frequency ; aggregated model ; decomposition ; stability ; dynamic distribution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A time decomposition technique is suggested for large-database (DB) models. The problem of network aggregation is studied and the results used to create a meaningful decomposed model. Decomposition conditions and assumptions are discussed and illustrated by examples. A practical operating schedule is presented for the time-separated DB model. The schedule uses a sequence of decomposed models, which are to be constructed recursively. The application of the time separation technique for large-DB models is presented in the form of a closed-loop algorithm. The problem of decomposition stability with respect to variations in time constants is considered as well. Two alternative approaches to the problem are suggested. For a probabilistic approach, practical approximate formulas are obtained for subsystem time constants and recommendations are made with respect to the decomposition structure. An approximate performance analysis is done for both standard and time-decomposed models. A comparison of the results is given.
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    Learning to Understand Information on the Internet: An Example-Based Approach (1997)
    Springer
    Journal of intelligent information systems 8 (1997), S. 133-153 
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    ISSN: 1573-7675
    Keywords: machine learning ; internet
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The explosive growth of the Web has made intelligent softwareassistants increasingly necessary for ordinary computer users. Bothtraditional approaches—search engines, hierarchical indices—andintelligent software agents require significant amounts of humaneffort to keep up with the Web. As an alternative, we investigate theproblem of automatically learning to interact with informationsources on the Internet. We report on ShopBotand ILA , two implemented agents that learn touse such resources. ShopBot learns how to extract information from onlinevendors using only minimal knowledge about product domains. Giventhe home pages of several online stores, ShopBotautonomously learns how to shop at those vendors. After its learningis complete, ShopBot is able to speedily visitover a dozen software stores and CD vendors, extract productinformation, and summarize the results for the user. ILAlearns to translate information from Internetsources into its own internal concepts. ILAbuilds a model of an information source that specifies the translation between the source's output and ILA 's model of the world. ILA iscapable of leveraging a small amount of knowledge about a domain tolearn models of many information sources. We show that ILA 's learning is fast and accurate, requiring only a smallnumber of queries per information source.
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    URL: http://dx.doi.org/10.1023/A:1008672508721
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    Splitting Methods for Three-Dimensional Transport Models with Interaction Terms (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 215-231 
    Details
    ISSN: 1573-7691
    Keywords: Transport models ; shallow water ; splitting methods ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We investigate the use of splitting methods for the numerical integration of three-dimensional transport-chemistry models. In particular, we investigate various possibilities for the time discretization that can take advantage of the parallelization and vectorization facilities offered by multi-processor vector computers. To suppress wiggles in the numerical solution, we use third-order, upwind-biased discretization of the advection terms, resulting in a five-point coupling in each direction. As an alternative to the usual splitting functions, such as co-ordinate splitting or operator splitting, we consider a splitting function that is based on a three-coloured hopscotch-type splitting in the horizontal direction, whereas full coupling is retained in the vertical direction. Advantages of this splitting function are the easy application of domain decomposition techniques and unconditional stability in the vertical, which is an important property for transport in shallow water. The splitting method is obtained by combining the hopscotch-type splitting function with various second-order splitting formulae from the literature. Although some of the resulting methods are highly accurate, their stability behaviour (due to horizontal advection) is quite poor. Therefore we also discuss several new splitting formulae with the aim to improve the stability characteristics. It turns out that this is possible indeed, but the price to pay is a reduction of the accuracy. Therefore, such methods are to be preferred if accuracy is less crucial than stability; such a situation is frequently encountered in solving transport problems. As part of the project TRUST (Transport and Reactions Unified by Splitting Techniques), preliminary versions of the schemes are implemented on the Cray C98 4256 computer and are available for benchmarking.
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    URL: http://dx.doi.org/10.1023/A:1025645326705
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    A Generalized Peaceman–Rachford ADI Scheme for Solving Two-Dimensional Parabolic Differential Equations (1997)
    Springer
    Journal of scientific computing 12 (1997), S. 353-360 
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    ISSN: 1573-7691
    Keywords: Alternating-direction implicit ; difference scheme ; stability ; convergence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A generalized Peaceman–Rachford alternating-direction implicit (ADI) scheme for solving two-dimensional parabolic differential equations has been developed based on the idea of regularized difference scheme. It is to be very well to simulate fast transient phenomena and to efficiently capture steady state solutions of parabolic differential equations. Numerical example is illustrated.
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    URL: http://dx.doi.org/10.1023/A:1025631211217
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    Constrained Learning in Neural Networks: Application to Stable Factorization of 2-D Polynomials (1998)
    Springer
    Neural processing letters 7 (1998), S. 5-14 
    Details
    ISSN: 1573-773X
    Keywords: constrained learning ; factorization ; feedforward networks ; IIR filters ; polynomials ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Adaptive artificial neural network techniques are introduced and applied to the factorization of 2-D second order polynomials. The proposed neural network is trained using a constrained learning algorithm that achieves minimization of the usual mean square error criterion along with simultaneous satisfaction of multiple equality and inequality constraints between the polynomial coefficients. Using this method, we are able to obtain good approximate solutions for non-factorable polynomials. By incorporating stability constraints into the formalism, our method can be successfully used for the realization of stable 2-D second order IIR filters in cascade form.
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    URL: http://dx.doi.org/10.1023/A:1009655902122
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    Hierarchical integration of sensor data and contextual information for automatic target recognition (1995)
    Springer
    Applied intelligence 5 (1995), S. 269-290 
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    ISSN: 1573-7497
    Keywords: automatic target recognition ; machine learning ; abductive polynomial networks ; expert systems ; information fusion
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Real-time assessment of high-value targets is an ongoing challenge for the defense community. Many automatic target recognition (ATR) approaches exist, each with specific advantages and limitations. An ATR system is presented here that integrates machine learning, expert systems, and other advanced image understanding concepts. The ATR system employs a hierarchical strategy relying primarily on abductive polynomial networks at each level of recognition. Advanced feature extraction algorithms are used at each level for pixel characterization and target description. Polynomial networks process feature data and situational information, providing input for subsequent levels of processing. An expert system coordinates individual recognition modules. Heuristic processing of object likelihood estimates is also discussed. Here, separate estimators determine the likelihood that an object belongs to a particular class. Heuristic knowledge to resolve ambiguities that occur when more than one class appears likely is discussed. In addition, a comparison of model-based recognition with the primary polynomial network approach is presented. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of known targets. Each approach has advantages and limitations that should be considered for a specific implementation. This ATR approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions, while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery; and the compact, real-time processing capability provided by abductive polynomial networks.
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    URL: http://dx.doi.org/10.1007/BF00872226
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    Average-case improvements when integrating ML and KA (1996)
    Springer
    Applied intelligence 6 (1996), S. 87-99 
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    ISSN: 1573-7497
    Keywords: machine learning ; knowledge acquisition ; integration ; models ; knowledge-based expert systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we develop a mathematical analysis, based on empirical measurements, of expected average improvements when integrating Machine Learning and Knowledge Acquisition systems in real-life domains. The analysis is based on the characteristics of component systems and combining techniques. Important characteristics include the accuracy of component systems, the degree to which component systems complement each other's weaknesses, and the ability of the combining mechanism to make good choices among competing component systems. Empirical measurements in a real-life application, in the Sendzimir rolling mill, have shown that integrating both approaches enables significant improvements. Improvements when combining systems in two oncological domains were smaller, yet positive again. Analytical average-case integrated models consisting of two systems are introduced. Conditions for improvements over the best, average and the worst system are established and the expected gains are analytically computed based on expected performances. Models strongly suggest that a reasonable integration of two systems offers significant improvements over the best single system in many or even most real-life domains.
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    URL: http://dx.doi.org/10.1007/BF00117810
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    Comparative Performance of Rule Quality Measures in an Induction System (1997)
    Springer
    Applied intelligence 7 (1997), S. 113-124 
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    ISSN: 1573-7497
    Keywords: intelligent manufacturing ; rule quality ; machine learning ; induction ; post-processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper addresses an important problem related to the use ofinduction systems in analyzing real world data. The problem is thequality and reliability of the rules generated by the systems.~Wediscuss the significance of having a reliable and efficient rule quality measure. Such a measure can provide useful support ininterpreting, ranking and applying the rules generated by aninduction system. A number of rule quality and statistical measuresare selected from the literature and their performance is evaluatedon four sets of semiconductor data. The primary goal of thistesting and evaluation has been to investigate the performance ofthese quality measures based on: (i) accuracy, (ii) coverage, (iii)positive error ratio, and (iv) negative error ratio of the ruleselected by each measure. Moreover, the sensitivity of these qualitymeasures to different data distributions is examined. Inconclusion, we recommend Cohen‘s statistic as being the best qualitymeasure examined for the domain. Finally, we explain some future workto be done in this area.
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    URL: http://dx.doi.org/10.1023/A:1008293727412
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    A Fuzzy Diagnostic Model and Its Application in Automotive Engineering Diagnosis (1998)
    Springer
    Applied intelligence 9 (1998), S. 231-243 
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    ISSN: 1573-7497
    Keywords: fuzzy logic ; machine learning ; fault diagnosis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a fuzzy diagnostic model that contains a fast fuzzy rule generation algorithm and a priority rule based inference engine. The fuzzy diagnostic model has been implemented in a fuzzy diagnostic system for the End-of-Line test at automobile assembly plants and the implemented system has been tested extensively and its performance is presented.
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    URL: http://dx.doi.org/10.1023/A:1008315803848
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    Incremental Feature Selection (1998)
    Springer
    Applied intelligence 9 (1998), S. 217-230 
    Details
    ISSN: 1573-7497
    Keywords: pattern recognition ; machine learning ; feature selection ; dimensionality reduction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is large and can scale up without sacrificing the quality of selected features.
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    URL: http://dx.doi.org/10.1023/A:1008363719778
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    Inductive specification recovery: Understanding software by learning from example behaviors (1995)
    Springer
    Automated software engineering 2 (1995), S. 107-129 
    Details
    ISSN: 1573-7535
    Keywords: induction ; machine learning ; reverse engineering ; Datalog
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We describe a technique for extracting specifications from software using machine learning techniques. In our proposed technique, instrumented code is run on a number of representative test cases, generating examples of its behavior. Inductive learning techniques are then used to generalize these examples, forming a general description of some aspect of the system's behavior. A case study is presented in which this “inductive specification recovery” method is used to find Datalog specifications forC code that implements database views, in the context of a large real-world software system. It is demonstrated that off-the-shelf inductive logic programming methods can be successfully used for specification recovery in this domain, but that these methods can be substantially improved by adapting them more closely to the task at hand.
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    URL: http://dx.doi.org/10.1007/BF00871824
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    Data Mining in Large Databases Using Domain Generalization Graphs (1999)
    Springer
    Journal of intelligent information systems 13 (1999), S. 195-234 
    Details
    ISSN: 1573-7675
    Keywords: data mining ; knowledge discovery ; machine learning ; knowledge representation ; attribute-oriented generalization ; domain generalization graphs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Attribute-oriented generalization summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We introduce domain generalization graphs for controlling the generalization of a set of attributes and show how they are constructed. We then present serial and parallel versions of the Multi-Attribute Generalization algorithm for traversing the generalization state space described by joining the domain generalization graphs for multiple attributes. Based upon a generate-and-test approach, the algorithm generates all possible summaries consistent with the domain generalization graphs. Our experimental results show that significant speedups are possible by partitioning path combinations from the DGGs across multiple processors. We also rank the interestingness of the resulting summaries using measures based upon variance and relative entropy. Our experimental results also show that these measures provide an effective basis for analyzing summary data generated from relational databases. Variance appears more useful because it tends to rank the less complex summaries (i.e., those with few attributes and/or tuples) as more interesting.
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    URL: http://dx.doi.org/10.1023/A:1008769516670
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    On the Accuracy of Meta-learning for Scalable Data Mining (1997)
    Springer
    Journal of intelligent information systems 8 (1997), S. 5-28 
    Details
    ISSN: 1573-7675
    Keywords: machine learning ; meta-learning ; scalability ; data mining ; classifiers
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, wedescribe a general approach to scaling data mining applications thatwe have come to call meta-learning. Meta-Learningrefers to a general strategy that seeks to learn how to combine anumber of separate learning processes in an intelligent fashion. Wedesire a meta-learning architecture that exhibits two key behaviors.First, the meta-learning strategy must produce an accurate final classification system. This means that a meta-learning architecturemust produce a final outcome that is at least as accurate as aconventional learning algorithm applied to all available data.Second, it must be fast, relative to an individual sequential learningalgorithm when applied to massive databases of examples, and operatein a reasonable amount of time. This paper focussed primarily onissues related to the accuracy and efficacy of meta-learning as ageneral strategy. A number of empirical results are presenteddemonstrating that meta-learning is technically feasible in wide-area,network computing environments.
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    URL: http://dx.doi.org/10.1023/A:1008640732416
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    Neural Networks in Non-Euclidean Spaces (1999)
    Springer
    Neural processing letters 10 (1999), S. 201-210 
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    ISSN: 1573-773X
    Keywords: neural networks ; learning ; minimal distance methods ; similarity-based methods ; machine learning ; interpretation of neural functions ; classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons providing decision borders using combinations of soft hyperplanes. The weighted fun-in activation function may be replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model. Non-Euclidean distance functions may also be introduced by normalization of the input vectors into an extended feature space. Both approaches influence the shapes of decision borders dramatically. An illustrative example showing these changes is provided.
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    URL: http://dx.doi.org/10.1023/A:1018728407584
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    Efficient estimation and model selection in large graphical models (1996)
    Springer
    Statistics and computing 6 (1996), S. 313-323 
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    ISSN: 1573-1375
    Keywords: Graphical models ; probabilistic expert systems ; machine learning ; Markov models ; causal structure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract We develop a computationally efficient method to determine the interaction structure in a multidimensional binary sample. We use an interaction model based on orthogonal functions, and give a result on independence properties in this model. Using this result we develop an efficient approximation algorithm for estimating the parameters in a given undirected model. To find the best model, we use a heuristic search algorithm in which the structure is determined incrementally. We also give an algorithm for reconstructing the causal directions, if such exist. We demonstrate that together these algorithms are capable of discovering almost all of the true structure for a problem with 121 variables, including many of the directions.
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    URL: http://dx.doi.org/10.1007/BF00143552
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    Making Beliefs Coherentl. The Subtraction and Addition Strategies. (1998)
    Springer
    Journal of logic, language and information 7 (1998), S. 143-163 
    Details
    ISSN: 1572-9583
    Keywords: Belief revision ; consolidation ; coherence ; stability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Linguistics and Literary Studies , Computer Science
    Notes: Abstract The notion of epistemic coherence is interpreted as involving not only consistency but also stability. The problem how to consolidate a belief system, i.e., revise it so that it becomes coherent, is studied axiomatically as well as in terms of set-theoretical constructions. Representation theorems are given for subtractive consolidation (where coherence is obtained by deleting beliefs) and additive consolidation (where coherence is obtained by adding beliefs).
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    URL: http://dx.doi.org/10.1023/A:1008274704871
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    INSTRUCT: Modeling students by asking questions (1996)
    Springer
    User modeling and user adapted interaction 6 (1996), S. 273-302 
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    ISSN: 1573-1391
    Keywords: student modeling ; intelligent tutoring systems ; machine learning ; procedure induction from traces ; model tracing ; reconstructive modeling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper reports an approach to inducing models of procedural skills from observed student performance. The approach, referred to as INSTRUCT, builds on two well-known techniques, reconstructive modeling and model tracing, at the same time avoiding their major pitfalls. INSTRUCT does not require prior empirical knowledge of student errors and is also neutral with respect to pedagogy and reasoning strategies applied by the student. Pedagogical actions and the student model are generated on-line, which allows for dynamic adaptation of instruction, problem generation and immediate feedback on student's errors. Furthermore, the approach is not only incremental but truly active, since it involves students in explicit dialogues about problem-solving decisions. Student behaviour is used as a source of information for user modeling and to compensate for the unreliability of the student model. INSTRUCT uses both implicit information about the steps the student performed or the explanations he or she asked for, and explicit information gained from the student's answers to direct question about operations being performed. Domain knowledge and the user model are used to focus the search on the portion of the problem space the student is likely to traverse while solving the problem at hand. The approach presented is examined in the context of SINT, an ITS for the domain of symbolic integration.
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    URL: http://dx.doi.org/10.1007/BF00213185
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    Feature Based Modelling: A methodology for producing coherent, consistent, dynamically changing models of agents' competencies (1995)
    Springer
    User modeling and user adapted interaction 5 (1995), S. 117-150 
    Details
    ISSN: 1573-1391
    Keywords: Student modelling ; machine learning ; modelling competency
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.
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    URL: http://dx.doi.org/10.1007/BF01099758
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    Control-Sensitive Feature Selection for Lazy Learners (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 227-253 
    Details
    ISSN: 1573-7462
    Keywords: lazy learning ; feature selection ; nearest neighbor ; induction ; machine learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract High sensitivity to irrelevant features is arguably the main shortcoming of simple lazy learners. In response to it, many feature selection methods have been proposed, including forward sequential selection (FSS) and backward sequential selection (BSS). Although they often produce substantial improvements in accuracy, these methods select the same set of relevant features everywhere in the instance space, and thus represent only a partial solution to the problem. In general, some features will be relevant only in some parts of the space; deleting them may hurt accuracy in those parts, but selecting them will have the same effect in parts where they are irrelevant. This article introduces RC, a new feature selection algorithm that uses a clustering-like approach to select sets of locally relevant features (i.e., the features it selects may vary from one instance to another). Experiments in a large number of domains from the UCI repository show that RC almost always improves accuracy with respect to FSS and BSS, often with high significance. A study using artificial domains confirms the hypothesis that this difference in performance is due to RC's context sensitivity, and also suggests conditions where this sensitivity will and will not be an advantage. Another feature of RC is that it is faster than FSS and BSS, often by an order of magnitude or more.
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    URL: http://dx.doi.org/10.1023/A:1006508722917
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    Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming (1998)
    Springer
    Applied intelligence 8 (1998), S. 33-41 
    Details
    ISSN: 1573-7497
    Keywords: genetic programming ; genetic algorithms ; computational genetics ; machine learning ; adaptive systems ; mobile robot ; robotics ; robot ; wall-following
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.
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    URL: http://dx.doi.org/10.1023/A:1008216530547
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    Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers (1998)
    Springer
    Autonomous robots 5 (1998), S. 317-334 
    Details
    ISSN: 1573-7527
    Keywords: neural network controllers ; machine learning ; innateness ; biologically inspired robotics ; quantification in robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The aim was to investigate a method of developing mobile robot controllers based on ideas about how plastic neural systems adapt to their environment by extracting regularities from the amalgamated behavior of inflexible (nonplastic) innate subsystems interacting with the world. Incremental bootstrapping of neural network controllers was examined. The objective was twofold. First, to develop and evaluate the use of prewired or innate robot controllers to bootstrap backpropagation learning for Multilayer Perceptron (MLP) controllers. Second, to develop and evaluate a new MLP controller trained on the back of another bootstrapped controller. The experimental hypothesis was that MLPs would improve on the performance of controllers used to train them. The performances of the innate and bootstrapped MLP controllers were compared in eight experiments on the tasks of avoiding obstacles and finding goals. Four quantitative measures were employed: the number of sensorimotor loops required to complete a task; the distance traveled; the mean distance from walls and obstacles; the smoothness of travel. The overall pattern of results from statistical analyses of these quantities supported the hypothesis; the MLP controllers completed the tasks faster, smoother, and steered further from obstacles and walls than their innate teachers. In particular, a single MLP controller incrementally bootstrapped by a MLP subsumption controller was superior to the others.
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    URL: http://dx.doi.org/10.1023/A:1008862423185
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