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  • Articles  (27)
  • 550 - Earth sciences
  • Electronic structure and strongly correlated systems
  • Engineering General
  • uncertainty
  • Computer Science  (27)
  • 1
    Electronic Resource
    Electronic Resource
    Entropy-Based Markov Chains for Multisensor Fusion (2000)
    Springer
    Journal of intelligent and robotic systems 29 (2000), S. 161-189 
    Details
    ISSN: 1573-0409
    Keywords: decision making ; entropy ; Markov chains ; multisensor fusion ; uncertainty
    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 proposes an entropy based Markov chain (EMC) fusion technique and demonstrates its applications in multisensor fusion. Self-entropy and conditional entropy, which measure how uncertain a sensor is about its own observation and joint observations respectively, are adopted. We use Markov chain as an observation combination process because of two major reasons: (a) the consensus output is a linear combination of the weighted local observations; and (b) the weight is the transition probability assigned by one sensor to another sensor. Experimental results show that the proposed approach can reduce the measurement uncertainty by aggregating multiple observations. The major benefits of this approach are: (a) single observation distributions and joint observation distributions between any two sensors are represented in polynomial form; (b) the consensus output is the linear combination of the weighted observations; and (c) the approach suppresses noisy and unreliable observations in the combination process.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008138126506
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  • 2
    Electronic Resource
    Electronic Resource
    a qualitative-connectionist approach to Robotic Spatial Planning: the Peg-in-Hole case study (2000)
    Springer
    Spatial cognition and computation 2 (2000), S. 51-76 
    Details
    ISSN: 1542-7633
    Keywords: connectionist learning ; fine motion ; qualitative spatial reasoning ; robotics ; uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Psychology
    Notes: Abstract Qualitative spatial reasoning foractual robots in real-world environments mustnecessarily involve perceptive knowledge, sincecomplete a-priori information of the outerworld can never be assumed, not even at aqualitative level. In this paper a contributionis made towards the integration of quantitativedata – namely, sensor signals – into a higherlevel qualitative plan. This integrationincludes the use of neural networks to learnhow to map complex perceptual signals into aqualitative description. Sensors are used toacquire actual knowledge of the environment andproperly identify, with the help of aconnectionist system, the real state of thesystem. The approach is presented in theframework of robotic tasks involving contactfor which the most informative perception comesfrom force/torque sensors. Empirical simulationresults are provided for the chamferlesstwo-dimensional peg-in-hole insertion modelwith friction. The advantages of learningapproaches over geometric model-basedtechniques are discussed: our approach issimple but robust against unpredictable changesof task parameters, and it exhibits agracefully degrading behavior and on-lineadaptation to new task conditions. Anenhancement to incorporate a measure ofconfidence of the network is also presented.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1011453027703
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  • 3
    Electronic Resource
    Electronic Resource
    Another Look at the Logical Uncertainty Principle (2000)
    Springer
    Information retrieval 2 (2000), S. 17-26 
    Details
    ISSN: 1573-7659
    Keywords: uncertainty ; information theory ; Bayesian inference ; Ramsey test ; information retrieval model
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The Logical Uncertainty Principle is re-examined from the point of classical logic. Two interpretations are given, an objective one in terms of an axiomatic theory of information, and a subjective one based on Ramsey's theory of probability.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009969229281
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  • 4
    Electronic Resource
    Electronic Resource
    A Probabilistic Approach to Collaborative Multi-Robot Localization (2000)
    Springer
    Autonomous robots 8 (2000), S. 325-344 
    Details
    ISSN: 1573-7527
    Keywords: mobile robots ; localization ; multi-robot systems ; uncertainty
    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 statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008937911390
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  • 5
    Electronic Resource
    Electronic Resource
    Autonomous Pool Cleaning: Self Localization and Autonomous Navigation for Cleaning (2000)
    Springer
    Autonomous robots 9 (2000), S. 261-270 
    Details
    ISSN: 1573-7527
    Keywords: mobile robot ; underwater environment ; sonar system ; state estimation ; uncertainty
    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 Cleaning is a major problem associated with pools. Since the manual cleaning is tedious and boring there is an interest in automating the task. This paper presents methods for autonomous localization and navigation for a pool cleaner to enable full coverage of pools. Path following cannot be ensured through use of internal position estimation methods alone; therefore sensing is needed. Sensor based estimation enable automatic correction of slippage. For this application we use ultrasonic sonars. Based on an analysis of the overall task and performance of the system a strategy for cleaning/navigation is developed. For the automatic localization a Kalman filtering technique is proposed: the Kalman filter uses sonar measurements and a dynamic model of the robot to provide estimates of the pose of the pool cleaner. Using this localization method we derive an optimal control strategy for traversal of a pool. The system has been implemented and successfully tested on the “WEDAB400” pool cleaner.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008962901812
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  • 6
    Electronic Resource
    Electronic Resource
    Stabilization of Nonholonomic Robotic Systems Using Adaptation and Homogeneous Feedback (1999)
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 1-27 
    Details
    ISSN: 1573-0409
    Keywords: nonholonomic systems ; stabilization ; uncertainty ; adaptive control ; robotic systems ; homogeneous feedback ; Lyapunov methods
    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 considers the problem of stabilizing nonholonomic robotic systems in the presence of uncertainty regarding the system dynamic model. It is proposed that a simple and effective solution to this problem can be obtained by combining ideas from homogeneous system theory and adaptive control theory. Thus each of the proposed control systems consists of two subsystems: a (homogeneous) kinematic stabilization strategy which generates a desired velocity trajectory for the nonholonomic system, and an adaptive control scheme which ensures that this velocity trajectory is accurately tracked. This approach is shown to provide arbitrarily accurate stabilization to any desired configuration and can be implemented without knowledge of the system dynamic model. Moreover, it is demonstrated that exponential rates of convergence can be achieved with this methodology. The efficacy of the proposed stabilization strategies is illustrated through extensive computer simulations with nonholonomic robotic systems arising from explicit constraints on the system kinematics and from symmetries of the system dynamics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008011519198
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  • 7
    Electronic Resource
    Electronic Resource
    Production Games under Uncertainty (1999)
    Springer
    Computational economics 14 (1999), S. 237-253 
    Details
    ISSN: 1572-9974
    Keywords: cooperative games ; production ; core ; uncertainty ; stochastic programming ; distribution
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract The main objects below are transferable-utility games in which each agent faces an optimization problem, briefly called production planning, constrained by his resource endowment. Coalitions can pool members' resources. Such production games are here extended to accommodate uncertainty about events not known ex ante. Planning then takes the form of two-stage stochastic programming. Core solutions are sought, described, and computed via aggregate dual programs. The analysis is motivated by practical applications. Examples include stochastic production and regional distribution with random demand and supply, illustrated by a numerical example.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008720525884
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  • 8
    Electronic Resource
    Electronic Resource
    Traversability Analysis and Path Planning for a Planetary Rover (1999)
    Springer
    Autonomous robots 6 (1999), S. 131-146 
    Details
    ISSN: 1573-7527
    Keywords: mobile robots ; sensor fusion ; terrain mapping ; obstacle avoidance ; uncertainty
    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 method of analyzing three-dimensional data such as might be produced by stereo vision or a laser range finder in order to plan a path for a vehicle such as a Mars rover is described. In order to produce robust results from data that is sparse and of varying accuracy, the method takes into account the accuracy of each data point, as represented by its covariance matrix. It computes estimates of smoothed and interpolated height, slope, and roughness at equally spaced horizontal intervals, as well as accuracy estimates of these quantities. From this data, a cost function is computed that takes into account both the distance traveled and the probability that each region is traversable. A parallel search algorithm that finds the path of minimum cost also is described. Examples using real data are presented.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008831426966
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  • 9
    Electronic Resource
    Electronic Resource
    Uncertainty Propagation in Model-Based Recognition (1998)
    Springer
    International journal of computer vision 27 (1998), S. 127-159 
    Details
    ISSN: 1573-1405
    Keywords: model-based vision ; object recognition ; alignment ; noise ; uncertainty ; error propagation ; linear programming ; perspective ; scaled-orthographic ; bounded error ; Gaussian error
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Robust recognition systems require a careful understanding of the effects of error in sensed features. In model-based recognition, matches between model features and sensed image features typically are used to compute a model pose and then project the unmatched model features into the image. The error in the image features results in uncertainty in the projected model features. We first show how error propagates when poses are based on three pairs of 3D model and 2D image points. In particular, we show how to simply and efficiently compute the distributed region in the image where an unmatched model point might appear, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. Next, we provide geometric and experimental analyses to indicate when this linear approximation will succeed and when it will fail. Then, based on the linear approximation, we show how we can utilize Linear Programming to compute bounded propagated error regions for any number of initial matches. Finally, we use these results to extend, from two-dimensional to three-dimensional objects, robust implementations of alignment, interpretation-tree search, and transformation clustering.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007989016491
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  • 10
    Electronic Resource
    Electronic Resource
    Sensor Fusion and Planning with Perception–Action Network (1997)
    Springer
    Journal of intelligent and robotic systems 19 (1997), S. 271-298 
    Details
    ISSN: 1573-0409
    Keywords: sensor fusion ; planning ; dynamic system ; robotic system ; uncertainty
    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 Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007933821405
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  • 11
    Electronic Resource
    Electronic Resource
    Interval Arithmetic for Input-output Models with Inexact Data (1997)
    Springer
    Computational economics 10 (1997), S. 89-100 
    Details
    ISSN: 1572-9974
    Keywords: input-output models ; uncertainty ; interval arithmetic.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract Input-output models are subject to uncertainty. If these models are solved without regard to the effects of the uncertainty the solutions can be substantially in error. Interval arithmetic offers a means by which the effects of this uncertainty can be assessed. They also offer a means of evaluating changes in the technical coefficients and a means of determining inverse important coefficients.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1017987931447
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  • 12
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    Statistical Themes and Lessons for Data Mining (1997)
    Springer
    Data mining and knowledge discovery 1 (1997), S. 11-28 
    Details
    ISSN: 1573-756X
    Keywords: statistics ; uncertainty ; modeling ; bias ; variance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Data mining is on the interface of Computer Science andStatistics, utilizing advances in both disciplines to make progressin extracting information from large databases. It is an emergingfield that has attracted much attention in a very short period oftime. This article highlights some statistical themes and lessonsthat are directly relevant to data mining and attempts to identifyopportunities where close cooperation between the statistical andcomputational communities might reasonably provide synergy forfurther progress in data analysis.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009773905005
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  • 13
    Electronic Resource
    Electronic Resource
    Real-World Robotics: Learning to Plan for Robust Execution (1996)
    Springer
    Machine learning 23 (1996), S. 121-161 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018274104280
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  • 14
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    A discrete event framework for autonomous observation under uncertainty (1996)
    Springer
    Journal of intelligent and robotic systems 16 (1996), S. 315-385 
    Details
    ISSN: 1573-0409
    Keywords: Robotics ; autonomous observation ; observation ; uncertainty ; discrete event systems ; observers ; vision
    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 work we establish a framework for the general problem of observation, which may be applied to different kinds of visual tasks. We construct ‘intelligent’ high-level control mechanisms for active visual recognition of different processes within a hybrid dynamic system. We address the problem of observing a manipulation process in order to illustrate the ideas and motive behind our framework. We use a discrete event dynamic system as a high-level structuring technique to model the manipulation system. The formulation utilizes the knowledge about the system and the different actions in order to solve the observer problem in an efficient, stable and practical manner. The model uses different tracking mechanisms so that the observer can ‘see’ the workspace of the manipulating robot. An automaton is developed for the hand/object interaction over time and a stabilizing observer is constructed. Low-level modules are developed for recognizing the visual ‘events’ that causes state transitions within the dynamic manipulation system in real time. A coarse quantization of the manipulation actions is used in order to attain an active, adaptive and goaldirected sensing mechanism. The formulation provides high-level symbolic interpretations of the scene under observation. The discrete event framework is augmented with mechanisms for recovering the continuous parametric evolution of the scene under observation and for asserting the state of the manipulation agent. This work examines closely the possibilities for errors, mistakes and uncertainties in the manipulation system, observer construction process and event identification mechanisms. We identify and suggest techniques for modeling these uncertainties. Ambiguities are allowed to develop and are resolved after finite time. Error recovery mechanisms are also devised. The computed uncertainties are utilized for navigating the observer automaton state space, asserting state transitions and developing a suitable tracking mechanism.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00270449
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  • 15
    Electronic Resource
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    Perception-based learning for motion in contact in task planning (1996)
    Springer
    Journal of intelligent and robotic systems 17 (1996), S. 283-308 
    Details
    ISSN: 1573-0409
    Keywords: manufacturing ; motion in contact ; force/torque sensors ; error detection ; plan monitoring ; uncertainty ; robotics ; neural networks
    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 error detection during motion in contact under uncertainty for robotic manufacturing tasks. In this approach, artificial neural networks are used for perception-based learning. The six force-and-torque signals from the wrist sensor of a robot arm are fed into the network. A self-organizing map is what learns the different contact states in an unsupervised way. The method is intended to work properly in complex real-world manufacturing environments, for which existent approaches based on geometric analytical models may not be feasible, or may be too difficult. It is used for different tasks involving motion in contact, particularly the peg-in-hole insertion task, and complex insertion or extraction operations in a flexible manufacturing system. Several real examples for these cases are presented.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00339665
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  • 16
    Electronic Resource
    Electronic Resource
    Real-world robotics: Learning to plan for robust execution (1996)
    Springer
    Machine learning 23 (1996), S. 121-161 
    Details
    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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00117442
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  • 17
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    Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty (1996)
    Springer
    Applied intelligence 6 (1996), S. 287-309 
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    ISSN: 1573-7497
    Keywords: constraint satisfaction problem ; possibility theory ; fuzzy restriction ; softness ; uncertainty ; preference ; priority
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instantiations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instantiations are provided owing to the relaxation of some constraints. Fuzzy constraints are thus flexible. CSP notions of consistency and k-consistency can be extended to this framework and the classical algorithms used in CSP resolution (e.g., tree search and filtering) can be adapted without losing much of their efficiency. Most classical theoretical results remain applicable to FCSPs. In the paper, various types of constraints are modelled in the same framework. The handling of uncertain parameters is carried out in the same setting because possibility theory can account for both preference and uncertainty. The presence of uncertain parameters leads to ill-defined CSPs, where the set of constraints which defines the problem is not precisely known.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00132735
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  • 18
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    Resource-bounded sensing and planning in autonomous systems (1996)
    Springer
    Autonomous robots 3 (1996), S. 31-48 
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    ISSN: 1573-7527
    Keywords: Real-time sensing and planning ; anytime algorithms ; deliberation scheduling ; meta-level reasoning ; uncertainty
    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 is concerned with the implications of limited computational resources and uncertainty on the design of autonomous systems. To address this problem, we redefine the principal role of sensor interpretation and planning processes. Following Agre and Chapman's plan-as-communication approach, sensing and planning are treated as computational processes that provide information to an execution architecture and thus improve the overall performance of the system. We argue that autonomous systems must be able to trade off the quality of this information with the computational resources required to produce it. Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for time-critical sensing and planning in robotic systems. In our earlier work, we introduced a compilation scheme for optimal composition of anytime algorithms. This paper demonstrates the applicability of the compilation technique to the construction of autonomous systems. The result is a flexible approach to construct systems that can operate robustly in real-time by exploiting the tradeoff between time and quality in planning, sensing and plan execution.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00162466
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  • 19
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    Intelligent reasoning using general knowledge to update specific information: A database approach (1995)
    Springer
    Journal of intelligent information systems 4 (1995), S. 281-304 
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    ISSN: 1573-7675
    Keywords: relational database ; mass assignments ; fuzzy sets ; uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes a database framework which is similar to a relational database in style but uses alternative knowledge structures to represent uncertain data. Two knowledge structures are used, the mass assignment to represent probabilistic information and fuzzy sets to hold subjective information. We describe how the query is modified such that the selection criteria is held in the form of specific knowledge which can be updated with the more general knowledge held in the database. The updating procedure has the effect of filling in uncertain or missing information such that a final solution can be found. The operations required to perform a query are generated automatically, optimisation is performed as the operations are determined. The output from the database is in the form of a distribution over a projection of the database domain space. An example is given where a database of sea vessels can be given uncertain or noisy evidence about the characteristics of a vessel and a distribution of the likelihood of each of the vessels can be determined from the evidence.
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    URL: http://dx.doi.org/10.1007/BF00961656
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  • 20
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    An intelligent control and decision approach for adaptive systems (1993)
    Springer
    Journal of intelligent and robotic systems 8 (1993), S. 63-76 
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    ISSN: 1573-0409
    Keywords: Intelligent control ; adaptive systems ; decision-making ; expert systems ; inexact reasoning ; uncertainty
    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 intelligent control and decision-making (ICD) approach that integrates expert systems technology with adaptive algorithms is presented. The controller parameters of adaptive systems can be determined using human expertise and knowledge, and they can also be adjusted based on active monitoring and identification. Decision-making, fine tuning and inexact reasoning provide the end-user and the control engineers with a natural and integrated methodology for use with intelligent control systems. Computer simulation results demonstrate the utility of the proposed technique that is an effective intelligent control and decision approach. The ICD system is implemented using a Lisp based expert system shell on an IBM PC.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01258640
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  • 21
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    On the learning of rule uncertainties and their integration into probabilistic knowledge bases (1993)
    Springer
    Journal of intelligent information systems 2 (1993), S. 245-264 
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    ISSN: 1573-7675
    Keywords: computational learning ; probability theory ; stratified Datalog ; uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules that he thinks are good indicators of whether a specific target concept is likely to occur. In a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Then, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information. Formally, we introduce the natural class of disjunctive probabilistic concepts and prove that this class is efficiently distribution-free learnable. The distribution-free learning model of probabilistic concepts was introduced by Kearns and Schapire and generalizes Valiant's probably approximately correct learning model. We show how to simulate the learned concepts in probabilistic knowledge bases which satisfy the laws of axiomatic probability theory. Finally, we combine the rule uncertainties with uncertain facts and prove the correctness of the combination under an independence assumption.
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    URL: http://dx.doi.org/10.1007/BF00962070
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  • 22
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    Planning of sensing tasks in an assembly environment (1991)
    Springer
    Journal of intelligent and robotic systems 4 (1991), S. 221-254 
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    ISSN: 1573-0409
    Keywords: Verification ; task-level programming ; robot vision ; uncertainty ; assembly planning ; fault tolerance ; classification ; prediction ; camera location
    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 purpose of this paper is to give an overview of past and recent work on planning sensing strategies for vision sensors. To achieve an economic use of robots in manufacturing, their programs must provide a high degree of fault-tolerance, security, and robustness to prevent unforeseen errors. Model errors (also termed uncertainties) are one of the most frequent reasons for such undesirable events. Robot systems can be made more reliable and fault-tolerant by providing them with capabilities of error detection and recovery, or error prevention. The latter may be achieved by reducing model errors using tactile and non-tactile sensors. The quality of a robot program synthesized by a task-level programming system depends on the accuracy of the model, since all information that is not explicitly given by the programmer must be derived from it. This means that the following questions have to be answered by the automatic task planner in order to plan non-tactile sensing strategies: (1) When do I have to use sensors to reduce uncertainty about the real world? (2) What do I have to use them for? (3) How do I have to use them to achieve the necessary information within an acceptable period of time? There are very few systems which deal broadly with the problem of robust robot programs, whereas there are numerous works on detail aspects of the field. The main approaches will be introduced and discussed in more detail. Finally, a new concept for generating sensor-integrated robust robot programs will be proposed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00303225
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    The representation of fuzzy relational production rules (1991)
    Springer
    Applied intelligence 1 (1991), S. 35-42 
    Details
    ISSN: 1573-7497
    Keywords: Fuzzy sets ; expert systems ; relational antecedents ; uncertainty
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We look at the representation within the framework of the approximate reasoning of relational type rules. A relational production rule consists of a rule in which one of the antecedent requirements involves the satisfaction of a relationship between two variables. An example of this type of rule is if lower and upper bounds are close then the uncertainty is low.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00117744
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  • 24
    Electronic Resource
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    Entropy-driven on-line control of autonomous robots (1989)
    Springer
    Journal of intelligent and robotic systems 2 (1989), S. 109-121 
    Details
    ISSN: 1573-0409
    Keywords: Entropy ; uncertainty ; evidential ; path planning ; recursive ; Newton ; attractors ; repellers
    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 new approach to on-line path planning is derived in this paper. The planning algorithm is motivated by robot navigation and manipulation tasks in uncertain, unstructured, dynamic environments. A minimum entropy evidential classifier is used to recognize targets and obstacles in the environment. An iterative Newton scheme is then used to generate a sequence of knot points that guide the motion of the robot. The acquisition and processing of sensory data continue during the motion, thus reducing the uncertainty about the environment. The classification of targets and obstacles is updated, and the path is replanned (locally) to adapt to those changes. A graphical tool based on the concept of Julia sets is used to ensure the predictability and smoothness of the paths.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00238684
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    Electronic Resource
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    Correctness criteria of some algorithms for uncertain reasoning using Incidence Calculus (1986)
    Springer
    Journal of automated reasoning 2 (1986), S. 109-126 
    Details
    ISSN: 1573-0670
    Keywords: Incidence Calculus ; probability ; uncertainty ; inference ; logic ; expert systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Incidence Calculus is a technique for associating uncertainty values with logical sentences. These uncertainty values are called incidences and they are sets of points, which may be thought of as representing equivalence classes of situations, Tarskian models, or possible worlds. Incidence Calculus was originally introduced in [1]. Incidence Calculus was designed to overcome various inherent problems with purely numeric mechanisms for uncertain reasoning [2]. In particular, incidences can represent the dependence between sentences, which numbers cannot, and hence Incidence Calculus can provide genuine, probabilistic reasoning. In this paper we prove soundness and completeness results for some algorithms introduced in [1] and hence satisfy some of the correctness criteria for Incidence Calculus. These algorithms can be used for probabilistic reasoning and to check the consistency of the subjective probabilities of sentences.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02432147
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    Incidence calculus: A mechanism for probabilistic reasoning (1985)
    Springer
    Journal of automated reasoning 1 (1985), S. 263-283 
    Details
    ISSN: 1573-0670
    Keywords: Incidence Calculus ; probability ; uncertainty ; logic ; expert systems ; inference
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Mechanisms for the automation of uncertainty are required for expert systems. Sometimes these mechanisms need to obey the properties of probabilistic reasoning. We argue that a purely numeric mechanism, like those proposed so far, cannot provide a probabilistic logic with truth functional connectives. We propose an alternative mechanism, Incidence Calculus, which is based on a representation of uncertainty using sets of points, which might represent situations models or possible worlds. Incidence Calculus does provide a probabilistic logic with truth functional connectives.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00244272
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    VIASKL: A computer program to evaluate the liquid scintillation counting efficiency and its associated uncertainty for K-L-atomic shell electron-capture nuclides
    Amsterdam : Elsevier
    Computer Physics Communications 44 (1987), S. 209-220 
    Details
    ISSN: 0010-4655
    Keywords: efficiency ; electron capture ; liquid scintillation ; uncertainty
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Computer Science , Physics
    Type of Medium: Electronic Resource
    URL: http://linkinghub.elsevier.com/retrieve/pii/0010-4655(87)90028-2
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