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  • Artikel  (84)
  • learning  (84)
  • Springer  (84)
  • American Association for the Advancement of Science
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  • Oxford University Press
  • Informatik  (84)
  • 1
    Digitale Medien
    Digitale Medien
    Springer
    AI & society 1 (1987), S. 93-101 
    ISSN: 1435-5655
    Schlagwort(e): information technology ; knowledge ; learning ; culture history ; social anthropology
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The social sciences lack concepts and theories for an understanding of what new information technology is doing to our society. The article sketches the outlines of a broad historical and comparative approach to this issue: ‘an anthropology of information technology’. At the base is the idea ofexternalisation of knowledge as a historical process. Three main epochs are characterised by externalisation of knowledge through a) spoken language and a social organisation of specialists, b) writing and c) computer programming. The impact of expert systems on learning is also discussed.
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Computers and the humanities 34 (2000), S. 279-295 
    ISSN: 1572-8412
    Schlagwort(e): accessibility ; classical ; data-bases ; languages (ancient) ; learning ; research ; teaching
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Medien- und Kommunikationswissenschaften, Kommunikationsdesign
    Notizen: Abstract The article offers a case study of the relationshipbetween current developments in Classical Studies andthe impact of computing and IT. The first sectionsummarises the main features of the Classical Studiesenvironment, especially the deep seated changes whichhave been taking place. These changes are then relatedto specific initiatives in Research, Teaching andLearning. The discussion is framed by a statement ofmicro-criteria for the evaluation of new developmentsand by reference to the macro-climate of debate aboutthe nature of cyberspace, especially the dichotomybetween conceptions of post-modern diversity and ofEnlightenment images of rational structures. It issuggested that these debates mirror those with whichthe discipline itself engages.
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  • 3
    Digitale Medien
    Digitale Medien
    Springer
    Minds and machines 3 (1993), S. 53-71 
    ISSN: 1572-8641
    Schlagwort(e): Symbolic AI ; connectionist AI ; connectionism ; neural networks ; learning ; reasoning ; expert networks ; expert systems ; symbolic models ; sub-symbolic models
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Philosophie
    Notizen: Abstract A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods into the symbolic domain. The connectionist representation learns and stores metaknowledge in highly connected subnetworks and domain knowledge in a sparsely connected expert network superstructure. The total connectivity of the neural network representation approximates that of real neural systems and hence avoids scaling and memory stability problems associated with other connectionist models.
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  • 4
    Digitale Medien
    Digitale Medien
    Springer
    Minds and machines 6 (1996), S. 507-523 
    ISSN: 1572-8641
    Schlagwort(e): Evolutionary psychology ; domain specificity ; modularity ; cognitive flexibility ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Philosophie
    Notizen: Abstract Jerry Fodor divides the mind into peripheral, domain-specific modules and a domaingeneral faculty of central cognition. John Tooby and Lisa Cosmides argue instead that the mind is modular all the way through; cognition consists of a multitude of domain-specific processes. But human thought has a flexible, innovative character that contrasts with the inflexible, stereotyped performances of modular systems. My goal is to discover how minds that are constructed on modular principles might come to exhibit cognitive versatility. Cognitive versatility is exhibited in the ability to learn from experience. How can this ability emerge from the resources made available by earlier stages of cognitive specialization without sacrificing the many benefits of modularization? A transition into versatile cognition occurred in the history of our species. A similar development which occurs within individual ontogeny provides clues about the phylogenetic changes. Annette Karmiloff-Smith describes an ontogenetic process in which the mind's representational resources are enriched. The key idea is that versatile thinkers have access to an inferentially integrated library of knowledge. A distinction between nonconceptual and conceptual representations helps to explain how smart minds can draw much finer-grained discriminations within their experience than can simple minds. This is an important though insufficient condition for cognitive versatility.
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  • 5
    Digitale Medien
    Digitale Medien
    Springer
    Minds and machines 7 (1997), S. 1-37 
    ISSN: 1572-8641
    Schlagwort(e): Connectionism ; systematicity ; learning ; language ; semantics
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Philosophie
    Notizen: Abstract Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words in syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems. Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but ‘classical’ insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical.
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  • 6
    Digitale Medien
    Digitale Medien
    Springer
    Computers and the humanities 27 (1993), S. 285-289 
    ISSN: 1572-8412
    Schlagwort(e): information technology ; computer ; education ; learning ; authoring languages ; IT concepts
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Medien- und Kommunikationswissenschaften, Kommunikationsdesign
    Notizen: Abstract Involving students in learning a small amount of programming language can enable the teacher to illustrate many of the important concepts of electronic information systems. It introduces them to experiential learning situations involving system design and operation, information handling and the man-machine interface. This paper describes how the authoring language PILOT has been used with arts and humanities undergraduates to increase their understanding of the power and potential of information technology, and to involve them in information problems that relate to their other humanities studies.
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  • 7
    Digitale Medien
    Digitale Medien
    Springer
    Artificial intelligence and law 3 (1995), S. 191-208 
    ISSN: 1572-8382
    Schlagwort(e): learning ; theory revision ; logic programming
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Rechtswissenschaft
    Notizen: Abstract In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law. The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case. By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case. Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call ‘theory reformulation’ the process adopted in this situation of ‘complete knowledge’. In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of ‘incomplete knowledge’, and call ‘theory revision’ the extended process. In order to fill in ‘knowledge gaps’ we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions. In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory. We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods.
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  • 8
    Digitale Medien
    Digitale Medien
    Springer
    Artificial intelligence and law 7 (1999), S. 115-128 
    ISSN: 1572-8382
    Schlagwort(e): analogy ; fuzzy logic ; learning ; legal formalism ; neural networks ; vagueness
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Rechtswissenschaft
    Notizen: Abstract Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.
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  • 9
    Digitale Medien
    Digitale Medien
    Springer
    Minds and machines 4 (1994), S. 317-332 
    ISSN: 1572-8641
    Schlagwort(e): Connectionism ; learning ; development ; recurrent networks ; unlearning ; catastrophic forgetting
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Philosophie
    Notizen: Abstract The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to ‘miss’ the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND ‘informed’ undersampling in early learning.
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  • 10
    Digitale Medien
    Digitale Medien
    Springer
    Minds and machines 7 (1997), S. 475-494 
    ISSN: 1572-8641
    Schlagwort(e): Emergence ; learning ; representation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Philosophie
    Notizen: Abstract The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a ‘fight-or-flight’ response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for closely-coupled agent/environment interaction.
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  • 11
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 6 (1992), S. 17-31 
    ISSN: 1573-0409
    Schlagwort(e): Robot ; learning ; control ; repetitive ; adaptive ; nonlinear
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The theory and implementation results of a recently developed class of adaptive and repetitive controllers used for motion control of robot manipulators are presented. The repetitive controller, which learns the input torque corresponding to a repetitive desired trajectory, requires no explicit knowledge of the manipulator equations of motion. The adaptive controller, on the other hand, which estimates the robot dynamic parameters on-line, may be used for more general trajectories but requires more detailed modeling information. Both schemes are computationally efficient and require no acceleration feedback of any kind; only standard position and velocity feedback information is utilized. The performance of both the adaptive and repetitive controllers was experimentally evaluated on an IBM 7545 robot. The experimental results of these controllers confirm a significant improvement in tracking accuracy over conventional ‘Computed Torque’ and PD controllers.
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  • 12
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 4 (1991), S. 43-53 
    ISSN: 1573-0409
    Schlagwort(e): Robots ; control ; learning ; tracking control ; computed-torque control ; robustness
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper derives a learning control law to achieve trajectory following for a robot manipulator. The controller consists of two parts, a computed torque servo for the rigid body terms that can be modelled and a learning law for the unmodelled dynamics. An advantage of this method is that bounds can be assigned to the position and velocity tracking errors.
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  • 13
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 4 (1991), S. 303-319 
    ISSN: 1573-0409
    Schlagwort(e): Telecommunications networks ; troubleshooting ; expert systems ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Our survey of some 40 network maintenance expert systems reveals theri main shortcoming, which is the difficulty to acquire troubleshooting knowledge both when initializing the expert system and after its deployment. Additionally, the state-of-the-art troubleshooting expert systems do not optimize troubleshooting cost. We present theAO * algorithm to generate a network troubleshooting expert system which minimizes the expected troubleshooting cost and learns better troubleshooting techniques during its operation.
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  • 14
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 13 (1995), S. 201-245 
    ISSN: 1573-0409
    Schlagwort(e): Planning systems ; theorem proving ; blackboard architectures ; assemblies ; rote planning ; learning ; automated manufacturing systems
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract AI and DAI based planning techniques, suitable for automated manufacturing systems are surveyed and compared. The relation of learning to planning is described and it is explained how learning may be used to improve planning. Several examples are presented. A complete reference list is provided.
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  • 15
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 331-349 
    ISSN: 1573-0409
    Schlagwort(e): constrained robots ; genetic algorithms ; learning ; friction compensation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction plus the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.
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  • 16
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 2 (1989), S. 361-379 
    ISSN: 1573-0409
    Schlagwort(e): Semantic variations ; learning ; classification ; knowledge-based systems ; artificial intelligence ; conceptual knowledge acquisition
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The notion of concept based on the semantics of objects is defined and illustrated. An underlying thread connecting a subset of concepts is identified. This class of concepts, called the Conceptual Transformer is defined and illustrated with real-world examples. This class finds a natural application in any area where objects can be characterized by functionality. Some interesting application areas are knowledge classification, manufacturing automation, and pattern synthesis. The salient features of this class are elaborated and a knowledge structure for representing concepts is proposed. The effect of these transformers on knowledge-directed classification, which results in the formation of virtual clusters, is examined in detail. We make use of examples from real life to bring out the efficacy of the proposed transformerbased, concept-directed classification.
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  • 17
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 19 (1997), S. 299-320 
    ISSN: 1573-0409
    Schlagwort(e): robotics ; motion planning ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Automatic motion planning is one of the basic modules that are needed to increase robot intelligence and usability. Unfortunately, the inherent complexity of motion planning has rendered traditional search algorithms incapable of solving every problem in real time. To circumvent this difficulty, we explore the alternative of allowing human operators to participate in the problem solving process. By having the human operator teach during difficult motion planning episodes, the robot should be able to learn and improve its own motion planning capability and gradually reduce its reliance on the human operator. In this paper, we present such a learning framework in which both human and robot can cooperate to achieve real-time automatic motion planning. To enable a deeper understanding of the framework in terms of performance, we present it as a simple learning algorithm and provide theoretical analysis of its behavior. In particular, we characterize the situations in which learning is useful, and provide quantitative bounds to predict the necessary training time and the maximum achievable speedup in planning time.
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  • 18
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 157-180 
    ISSN: 1573-0409
    Schlagwort(e): robot ; PID control ; neural networks ; learning ; generalization
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In this article, an approach for improving the performance of industrialrobots using multilayer feedforward neural networks is presented. Thecontroller based on this approach consists of two main components: a PIDcontrol and a neural network. The function of the neural network is tocomplement the PID control for the specific purpose of improving theperformance of the system over time. Analytical and experimental resultsconcerning this synthesis of neural networks and PID control are presented.The analytical results assert that the performance of PID-controlledindustrial robots can be improved through proper utilization of the learningand generalization ability of neural networks. The experimental results,obtained through actual implementation using a commercial industrial robot,demonstrate the effectiveness of such control synthesis for practicalapplications. The results of this work suggest that neural networks could beadded to existing PID-controlled industrial robots for performanceimprovement.
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  • 19
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 4 (1989), S. 339-345 
    ISSN: 0885-6125
    Schlagwort(e): Generic tasks ; learning ; knowledge acquisition ; task-structure
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract One of the old saws about learning in AI is that an agent can only learn what it can be told, i.e., the agent has to have a vocabulary for the target structure which is to be acquired by learning. What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. I long have argued that both the forms of declarative knowledge required for problem solving as well as problem-solving strategies are functions of the problem-solving task and have identified a family of generic tasks that can be used as building blocks for the construction of knowledge systems. In this editorial, I discuss the implication of this line of research for knowledge acquisition and learning.
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  • 20
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 33 (1998), S. 129-153 
    ISSN: 0885-6125
    Schlagwort(e): learning ; coordination ; multiagent systems
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Coordination is an essential technique in cooperative, distributed multiagent systems. However, sophisticated coordination strategies are not always cost-effective in all problem-solving situations. This paper presents a learning method to identify what information will improve coordination in specific problem-solving situations. Learning is accomplished by recording and analyzing traces of inferences after problem solving. The analysis identifies situations where inappropriate coordination strategies caused redundant activities, or the lack of timely execution of important activities, thus degrading system performance. To remedy this problem, situation-specific control rules are created which acquire additional nonlocal information about activities in the agent networks and then select another plan or another scheduling strategy. Examples from a real distributed problem-solving application involving diagnosis of a local area network are described.
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  • 21
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 10 (1993), S. 7-55 
    ISSN: 0885-6125
    Schlagwort(e): Derivational analogy ; program synthesis ; learning ; UNIX programming
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The feasibility of derivational analogy as a mechanism for improving problem-solving behavior has been shown for a variety of problem domains by several researchers. However, most of the implemented systems have been empirically evaluated in the restricted context of an already supplied base analog or on a few isolated examples. In this paper we describe a derivational analogy based system, APU, that synthesizes UNIX shell scripts from a high-level problem specification. APU uses top-down decomposition of problems, employing a hierarchical planner and a layered knowledge base of rules, and is able to speed up the derivation of programs by using derivational analogy. We assume that the problem specification is encoded in the vocabulary used by the rules. We describe APU's retrieval heuristics that exploit this assumption to automatically retrieve a good analog for a target problem from a case library, as well as its replay algorithm that enables it to effectively reuse the solution of an analogous problem to derive a solution for a new problem. We present experimental results to assess APU's performance, taking into account the cost of retrieving analogs from a sizable case library. We discuss the significance of the results and some of the issues in using derivational analogy to synthesize programs.
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  • 22
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 10 (1993), S. 341-363 
    ISSN: 0885-6125
    Schlagwort(e): Heuristic search ; case-based reasoning ; learning ; A* algorithm
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Case-Based Reasoning (CBR) is one of the emerging paradigms for designing intelligent systems. Preliminary studies indicate that the area is ripe for theoretical advances and innovative applications. Heuristic search is one of the most widely used techniques for obtaining optimal solutions to many real-world problems. We formulated the design of wastewater treatment systems as a heuristic search problem. In this article we identify some necessary properties of the heuristic search problems to be solved in the CBR paradigm. We designed a CBR system based on these observations and performed several experiments with the wastewater treatment problem. We compare the performance of the CBR system with the A* search algorithm.
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  • 23
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 20 (1995), S. 197-243 
    ISSN: 0885-6125
    Schlagwort(e): Bayesian networks ; learning ; Dirichlet ; likelihood equivalence ; maximum branching ; heuristic search
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence. We show that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen—a prior network—and a single measure of confidence for that network. Second, using these priors, we show how to compute the relative posterior probabilities of network structures given data. Third, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highest-scoring network structures in the special case where every node has at most k = 1 parent. For the general case (k 〉 1), which is NP-hard, we review heuristic search algorithms including local search, iterative local search, and simulated annealing. Finally, we describe a methodology for evaluating Bayesian-network learning algorithms, and apply this approach to a comparison of various approaches.
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  • 24
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 4 (1989), S. 339-345 
    ISSN: 0885-6125
    Schlagwort(e): Generic tasks ; learning ; knowledge acquisition ; task-structure
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract One of the old saws about learning in AI is that an agent can only learn what it can be told, i.e., the agent has to have a vocabulary for the target structure which is to be acquired by learning. What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. I long have argued that both the forms of declarative knowledge required for problem solving as well as problem-solving strategies are functions of the problem-solving task and have identified a family of generic tasks that can be used as building blocks for the construction of knowledge systems. In this editorial, I discuss the implication of this line of research for knowledge acquisition and learning.
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  • 25
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 20 (1995), S. 197-243 
    ISSN: 0885-6125
    Schlagwort(e): Bayesian networks ; learning ; Dirichlet ; likelihood equivalence ; maximum branching ; heuristic search
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption oflikelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence. We show that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen—aprior network—and a single measure of confidence for that network. Second, using these priors, we show how to compute the relative posterior probabilities of network structures given data. Third, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highest-scoring network structures in the special case where every node has at mostk=1 parent. For the general case (k〉1), which is NP-hard, we review heuristic search algorithms including local search, iterative local search, and simulated annealing. Finally, we describe a methodology for evaluating Bayesian-network learning algorithms, and apply this approach to a comparison of various approaches.
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  • 26
    Digitale Medien
    Digitale Medien
    Springer
    Machine learning 33 (1998), S. 155-177 
    ISSN: 0885-6125
    Schlagwort(e): learning ; multiagent ; coordination ; cooperative ; hill-climbing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A central issue in the design of cooperative multiagent systems is how to coordinate the behavior of the agents to meet the goals of the designer. Traditionally, this had been accomplished by hand-coding the coordination strategies. However, this task is complex due to the interactions that can take place among agents. Recent work in the area has focused on how strategies can be learned. Yet, many of these systems suffer from convergence, complexity and performance problems. This paper presents a new approach for learning multiagent coordination strategies that addresses these issues. The effectiveness of the technique is demonstrated using a synthetic domain and the predator and prey pursuit problem.
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  • 27
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    Machine learning 35 (1999), S. 155-185 
    ISSN: 0885-6125
    Schlagwort(e): reinforcement learning ; multi-agent systems ; planning ; evolutionary economics ; tragedy of the commons ; classifier systems ; agoric systems ; autonomous programming ; cognition ; artificial intelligence ; Hayek ; complex adaptive systems ; temporal difference learning ; evolutionary computation ; economic models of mind ; economic models of computation ; Blocks World ; reasoning ; learning ; computational learning theory ; learning to reason ; meta-reasoning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A market-based algorithm is presented which autonomously apportions complex tasks to multiple cooperating agents giving each agent the motivation of improving performance of the whole system. A specific model, called “The Hayek Machine” is proposed and tested on a simulated Blocks World (BW) planning problem. Hayek learns to solve more complex BW problems than any previous learning algorithm. Given intermediate reward and simple features, it has learned to efficiently solve arbitrary BW problems. The Hayek Machine can also be seen as a model of evolutionary economics.
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  • 28
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    Machine learning 10 (1993), S. 7-55 
    ISSN: 0885-6125
    Schlagwort(e): Derivational analogy ; program synthesis ; learning ; UNIX programming
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The feasibility of derivational analogy as a mechanism for improving problem-solving behavior has been shown for a variety of problem domains by several researchers. However, most of the implemented systems have been empirically evaluated in the restricted context of an already supplied base analog or on a few isolated examples. In this paper we describe a derivational analogy based system, APU, that synthesizes UNIX shell scripts from a high-level problem specification. APU uses top-down decomposition of problems, employing a hierarchical planner and a layered knowledge base of rules, and is able to speed up the derivation of programs by using derivational analogy. We assume that the problem specification is encoded in the vocabulary used by the rules. We describe APU's retrieval heuristics that exploit this assumption to automatically retrieve a good analog for a target problem from a case library, as well as its replay algorithm that enables it to effectively reuse the solution of an analogous problem to derive a solution for a new problem. We present experimental results to assess APU's performance, taking into account the cost of retrieving analogs from a sizable case library. We discuss the significance of the results and some of the issues in using derivational analogy to synthesize programs.
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  • 29
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    Machine learning 19 (1995), S. 133-152 
    ISSN: 0885-6125
    Schlagwort(e): learning ; natural language ; grammatical generalization ; robotics
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We are developing a theory of probabilistic language learning in the context of robotic instruction in elementary assembly actions. We describe the process of machine learning in terms of the various events that happen on a given trial, including the crucial association of words with internal representations of their meaning. Of central importance in learning is the generalization from utterances to grammatical forms. Our system derives a comprehension grammar for a superset of a natural language from pairs of verbal stimuli like Go to the screw! and corresponding internal representations of coerced actions. For the derivation of a grammar no knowledge of the language to be learned is assumed but only knowledge of an internal language. We present grammars for English, Chinese, and German generated from a finite sample of about 500 commands that are roughly equivalent across the three languages. All of the three grammars, which are context-free in form, accept an infinite set of commands in the given language.
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  • 30
    Digitale Medien
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    Machine learning 19 (1995), S. 133-152 
    ISSN: 0885-6125
    Schlagwort(e): learning ; natural language ; grammatical generalization ; robotics
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We are developing a theory of probabilistic language learning in the context of robotic instruction in elementary assembly actions. We describe the process of machine learning in terms of the various events that happen on a given trial, including the crucial association of words with internal representations of their meaning. Of central importance in learning is the generalization from utterances to grammatical forms. Our system derives a comprehension grammar for a superset of a natural language from pairs of verbal stimuli likeGo to the screw! and corresponding internal representations of coerced actions. For the derivation of a grammar no knowledge of the language to be learned is assumed but only knowledge of an internal language. We present grammars for English, Chinese, and German generated from a finite sample of about 500 commands that are roughly equivalent across the three languages. All of the three grammars, which are context-free in form, accept an infinite set of commands in the given language.
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  • 31
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    Soft computing 3 (1999), S. 162-173 
    ISSN: 1433-7479
    Schlagwort(e): Key words Bayesian networks ; learning ; stochastic simulation ; mutual information ; chi-square approximation.
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract  Stochastic independence is an idealized relationship located at one end of a continuum of values measuring degrees of dependence. Modeling real-world systems, we are often not interested in the distinction between exact independence and any degree of dependence, but between weak ignorable and strong substantial dependence. Good models map significant deviance from independence and neglect approximate independence or dependence weaker than a noise threshold. This intuition is applied to learning the structure of Bayes nets from data. We determine the conditional posterior probabilities of structures given that the degree of dependence at each of their nodes exceeds a critical noise level. Deviance from independence is measured by mutual information. The arc probabilities are set equal to the probability that the mutual information, provided by the neighbors of a node, exceeds a given threshold. A χ2 approximation for the probability density function of mutual information is used. A large number of network structures in which the arc probabilities are evaluated, is generated by stochastic simulation. Finally, the probabilities of the whole graph structures are obtained by combining the individual arc probabilities with the number of possible construction sequences compatible with the partial ordering of the graph. While selecting models with large deviance from independence results in simple networks with few but important links, selecting models with small deviance results in highly connected networks also containing less important links.
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  • 32
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    Computational economics 12 (1998), S. 171-191 
    ISSN: 1572-9974
    Schlagwort(e): hyperinflation ; bounded rationality ; rational expectations equilibria ; learning ; artificial neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Wirtschaftswissenschaften
    Notizen: Abstract We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be ‘substantively rational’. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow ‘procedurally rational’: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.
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  • 33
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    Computational economics 13 (1999), S. 41-60 
    ISSN: 1572-9974
    Schlagwort(e): genetic algorithms ; learning ; equilibrium selection ; heterogeneous beliefs
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Wirtschaftswissenschaften
    Notizen: Abstract We study a general equilibrium system where agents have heterogeneous beliefs concerning realizations of possible outcomes. The actual outcomes feed back into beliefs thus creating a complicated nonlinear system. Beliefs are updated via a genetic algorithm learning process which we interpret as representing communication among agents in the economy. We are able to illustrate a simple principle: genetic algorithms can be implemented so that they represent pure learning effects (i.e., beliefs updating based on realizations of endogenous variables in an environment with heterogeneous beliefs). Agents optimally solve their maximization problem at each date given their beliefs at each date. We report the results of a set of computational experiments in which we find that our population of artificial adaptive agents is usually able to coordinate their beliefs so as to achieve the Pareto superior rational expectations equilibrium of the model.
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  • 34
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    Spatial cognition and computation 2 (2000), S. 117-134 
    ISSN: 1542-7633
    Schlagwort(e): aging ; spatial abilities ; wayfinding ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Psychologie
    Notizen: Abstract Empirical relations among age, general spatialability as assessed by psychometric tests,wayfinding-related skills as assessed byexperimental tasks in the laboratory,environmental layout learning as assessed in afield experiment, and wayfinding behavior asobserved in a field experiment were modeled ina study involving 120 younger and 120 olderadults. The best-fitting model showed thatage-related differences in learningenvironmental layout were significantly, butnot exclusively, mediated by a single abilityfactor defined by psychometric tests. Knowledge of environmental layout was theexclusive mediator between general spatialability and wayfinding behavior. Thus, agedifferences in psychometric test performancewere found to be a major factor in accountingfor aging-related decline in learningenvironmental layout, but other variables notassessed in this study also play a significantrole.
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  • 35
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    Computational economics 14 (1999), S. 263-267 
    ISSN: 1572-9974
    Schlagwort(e): macroeconomics ; learning ; stochastic optimization ; numerical experiments
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Wirtschaftswissenschaften
    Notizen: Abstract Many macroeconomic policy exercises consider the mean values of parameter estimates but do not use the variances and covariances. One can argue that the uncertainty of these parameter estimates is sufficiently small that it can safely be ignored. Or one can take the position that this kind of uncertainty cannot be avoided no matter what one does. Thus it is just as well to ignore it while making policy decisions. In this paper we address both of these positions in the presence of learning and find that they are unconvincing. To the contrary, we find evidence that the potential damage from ignoring the variances and covariances of the parameter estimates is substantial and that taking them into account can improve matters.
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  • 36
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    Computational economics 2 (1989), S. 239-254 
    ISSN: 1572-9974
    Schlagwort(e): Time series modeling ; pattern recognition ; decision support systems ; learning ; fuzzy decision tree classifier ; knowledge-based approach
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Wirtschaftswissenschaften
    Notizen: Abstract In this paper, a new approach using pattern recognition techniques is suggested for time series modeling which means identification of a time series into one of autoregressive moving-average models. Its main recipe is that pattern is derived from a time series and classified into a suitable model via a notion of pattern matching. The pattern is obtained from extended sample autocorrelations of the time series. The pattern recognition techniques used are learning and decision tree classifier. Learning is used in combination with linear discriminants whose goal is to discriminate one pattern from another. Decision tree classifier divides decision procedures involved in time series modeling into simpler and local decisions at each node of a decision tree. To facilitate complex tree search, knowledge-based approach is used. To implement the idea, a scheme of decision support system is employed to develop a prototype system named PRTSM (Pattern Recognition-based Time Series Modeler). Experimental results with several examples show that a pattern recognition-based approach can yield a promising solution to the time series modeling.
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  • 37
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    Applied intelligence 13 (2000), S. 41-57 
    ISSN: 1573-7497
    Schlagwort(e): case-based reasoning ; planning ; local similarity metric ; learning ; temporal reasoning ; constraint satisfaction
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper describes an AI system for planning the first attack on a forest fire. This planning system is based on two major techniques, case-based reasoning, and constraint reasoning, and is part of a decision support system called CHARADE. CHARADE is aimed at supporting the user in the whole process of forest fire management. The novelty of the proposed approach is mainly due to the use of a local similarity metric for case-based reasoning and the integration with a constraint solver in charge of temporal reasoning.
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  • 38
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    Neural processing letters 10 (1999), S. 181-193 
    ISSN: 1573-773X
    Schlagwort(e): evolution ; learning ; ontogeny ; neural development ; structure optimization
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The interaction between learning and evolution has elicited much interest particularly among researchers who use evolutionary algorithms for the optimization of neural structures. In this article, we will propose an extension of the existing models by including a developmental phase – a growth process – of the neural network. In this way, we are able to examine the dynamical interaction between genetic information and information learned during development. Several measures are proposed to quantitatively examine the benefits and the effects of such an overlap between learning and evolution. The proposed model, which is based on the recursive encoding method for structure optimization of neural networks, is applied to the problem domain of time series prediction. Furthermore, comments are made on problem domains which associate growing networks (size) during development with problems of increasing complexity.
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  • 39
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    Neural processing letters 12 (2000), S. 115-128 
    ISSN: 1573-773X
    Schlagwort(e): evolutionary algorithms ; generalization ; learning ; neural networks ; optimization
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper proposes a new version of a method (G-Prop, genetic backpropagation) that attempts to solve the problem of finding appropriate initial weights and learning parameters for a single hidden layer Multilayer Perceptron (MLP) by combining an evolutionary algorithm (EA) and backpropagation (BP). The EA selects the MLP initial weights, the learning rate and changes the number of neurons in the hidden layer through the application of specific genetic operators, one of which is BP training. The EA works on the initial weights and structure of the MLP, which is then trained using QuickProp; thus G-Prop combines the advantages of the global search performed by the EA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop algorithm to several real-world and benchmark problems shows that MLPs evolved using G-Prop are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as QuickPropagation or RPROP, and other evolutive algorithms. It also shows some improvement over previous versions of the algorithm.
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  • 40
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    Neural processing letters 5 (1997), S. 19-24 
    ISSN: 1573-773X
    Schlagwort(e): Hessian eigenvalues ; information gain ; learning ; neural architectures
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We introduce a new analysis tool able to test the learning distribution inside an MLP network. This tool is based on the exploitation of specific indicators which measure the contribution of any interconnection graph subset to the information gain evolution. We present an application of this method to an efficient architecture synthesis algorithm.
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  • 41
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    Artificial intelligence review 10 (1996), S. 409-439 
    ISSN: 1573-7462
    Schlagwort(e): information retrieval ; logical inference ; fuzzy modal logic ; thesaurus ; learning ; relevance feedback
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Most inferential approaches to Information Retrieval (IR) have been investigated within the probabilistic framework. Although these approaches allow one to cope with the underlying uncertainty of inference in IR, the strict formalism of probability theory often confines our use of knowledge to statistical knowledge alone (e.g. connections between terms based on their co-occurrences). Human-defined knowledge (e.g. manual thesauri) can only be incorporated with difficulty. In this paper, based on a general idea proposed by van Rijsbergen, we first develop an inferential approach within a fuzzy modal logic framework. Differing from previous approaches, the logical component is emphasized and considered as the pillar in our approach. In addition, the flexibility of a fuzzy modal logic framework offers the possibility of incorporating human-defined knowledge in the inference process. After defining the model, we describe a method to incorporate a human-defined thesaurus into inference by taking user relevance feedback into consideration. Experiments on the CACM corpus using a general thesaurus of English, Wordnet, indicate a significant improvement in the system's performance.
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  • 42
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    Artificial intelligence review 9 (1995), S. 3-18 
    ISSN: 1573-7462
    Schlagwort(e): induction ; superstition ; learning ; behaviourism ; obsessive-compulsive disorder
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The behaviourist perspective on superstition is explained and an analogy is drawn between superstitious behaviour and the phenomenon of excessive branching in tree-based inductive learning algorithms operating under uncertainty. The argument is then extended to include cognitive aspects of learning and superstition. Further analogies are made with obsessive-compulsive disorder. Ways in which these phenomena might be simulated are indicated.
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  • 43
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    International journal of parallel programming 8 (1979), S. 219-238 
    ISSN: 1573-7640
    Schlagwort(e): Programming ; programming languages ; cognitive models ; program composition ; program comprehension ; debugging ; modification ; learning ; education ; information processing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper presents a cognitive framework for describing behaviors involved in program composition, comprehension, debugging, modification, and the acquisition of new programming concepts, skills, and knowledge. An information processing model is presented which includes a long-term store of semantic and syntactic knowledge, and a working memory in which problem solutions are constructed. New experimental evidence is presented to support the model of syntactic/semantic interaction.
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  • 44
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    Neural processing letters 12 (2000), S. 71-90 
    ISSN: 1573-773X
    Schlagwort(e): learning ; multiple-valued multiple-threshold functions ; multilinear separability ; partial order set ; perceptrons
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The (n,k,s)-perceptrons partition the input space V ⊂ R n into s+1 regions using s parallel hyperplanes. Their learning abilities are examined in this research paper. The previously studied homogeneous (n,k,k−1)-perceptron learning algorithm is generalized to the permutably homogeneous (n,k,s)-perceptron learning algorithm with guaranteed convergence property. We also introduce a high capacity learning method that learns any permutably homogeneously separable k-valued function given as input.
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  • 45
    ISSN: 1573-773X
    Schlagwort(e): classification of seismic events ; fuzzy logic ; half-distributed coding ; incomplete data ; learning ; multi-layer perceptron
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This letter presents a method for modelling and processing incomplete data in connectionist systems. The approach consists in applying a neuro-fuzzy coding to the input data of a neural network. After an introduction to the different kinds of imperfections, we propose a neuro-fuzzy coding in order to take incomplete data into account. We show the efficiency of this coding on the problem of the classification of seismic events. The results show that a neuro-fuzzy coding of the inputs of a neural network increases the performance and classifies incomplete data with little affect on the results.
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  • 46
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    Neural processing letters 11 (2000), S. 39-49 
    ISSN: 1573-773X
    Schlagwort(e): functional equations ; functional networks ; learning ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract In this paper, a minimax method for learning functional networks is presented. The idea of the method is to minimize themaximum absolute error between predicted and observed values. In addition, the invertible functions appearing in the modelare assumed to be linear convex combinations of invertible functions. This guarantees the invertibilityof the resulting approximations. The learning method leads to a linear programming problem and then: (a) the solution isobtained in a finite number of iterations, and (b) the global optimum is attained. The method is illustrated withseveral examples of applications, including the Hénon and Lozi series. The results show that the method outperforms standard least squares direct methods.
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  • 47
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    Neural processing letters 12 (2000), S. 239-246 
    ISSN: 1573-773X
    Schlagwort(e): complex numbers ; complex-valued neurons ; learning ; decision boundary
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper presents some results of an analysis on the decision boundaries of complex-valued neurons. The main results may be summarized as follows. (a) Weight parameters of a complex-valued neuron have a restriction which is concerned with two-dimensional motion. (b) The decision boundary of a complex-valued neuron consists of two hypersurfaces which intersect orthogonally, and divides a decision region into four equal sections.
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  • 48
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    Artificial intelligence review 10 (1996), S. 131-146 
    ISSN: 1573-7462
    Schlagwort(e): symbol grounding ; perception ; connectionist model ; neural networks ; hybrid system ; learning ; cognitive model
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper deals with symbol formation, from a cognitive point of view, through a connectionist model. To give an idea of our aim, let us consider the metaphor of learning to play tennis. Two knowledge forms are involved: - implicit knowledge, e.g. sensori-motor associations; this knowledge is subsymbolic - explicit knowledge, e.g. a teacher giving verbal advice, which makes use of symbols. Learned knowledge consists of a combination of subsymbolic and symbolic items. More than a juxtaposition, this combination involves grounding symbols into a subsymbolic substratum. This leads us to connectionist modelling which is considered as the common framework for both kinds of knowledge.
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  • 49
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    Applied intelligence 2 (1992), S. 47-73 
    ISSN: 1573-7497
    Schlagwort(e): Knowledge goals ; learning ; inference ; natural language understanding ; diagnostic problem solving
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience.
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  • 50
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    Applied intelligence 6 (1996), S. 205-213 
    ISSN: 1573-7497
    Schlagwort(e): financial markets ; trading ; forecasting ; learning ; hints
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.
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  • 51
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    Applied intelligence 7 (1997), S. 315-326 
    ISSN: 1573-7497
    Schlagwort(e): connectionist ; learning ; manipulation robots ; contact tasks ; impedance control ; recurrent network
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The goal of this paper is to consider the synthesis of learning impedance control using recurrent connectionist structures for on-line learning of robot dynamic uncertainties in the case of robot contact tasks. The connectionist structures are integrated in non-learning impedance control laws that are intended to improve the transient dynamic response immediately after the contact. The recurrent neural network as a part of hybrid learning control algorithms uses fast learning rules and available sensor information in order to improve the robotic performance progressively for a minimum possible number of learning epochs. Some simulation results of deburring process with the MANUTEC r3 robot are presented here in order to verify the effectiveness of the proposed control learning algorithms.
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  • 52
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    Information technology and management 1 (2000), S. 209-227 
    ISSN: 1573-7667
    Schlagwort(e): education ; virtual education ; learning ; web-based learning ; virtual learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The use of information technology to enhance teaching and learning processes has been practiced for a number of years now. However, the rapid growth in the use of the Internet has led to a new dimension in interactive and collaborative learning anytime and anyplace dynamically. With the explosion of “virtual education initiatives”, the question of the feasibility and success criteria for such projects quickly arises. To address the question in an organized way, we propose a project assessment based on critical success factors. Hence, in this article, we draw on a widely recognized critical success factor framework. We (slightly) adjust the framework to fit the special characteristics of virtual education initiatives, and apply to one case study, namely the virtual education initiative at the Faculty of Business at the City University of Hong Kong. The results suggest that the past success of the case is due to the adherence to the large majority of critical success factors. However, it also outlines some areas of concern. The paper concludes with a discussion on the strength and limitations of virtual learning environment as well as future directions.
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  • 53
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    Neural processing letters 10 (1999), S. 201-210 
    ISSN: 1573-773X
    Schlagwort(e): neural networks ; learning ; minimal distance methods ; similarity-based methods ; machine learning ; interpretation of neural functions ; classification
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: 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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  • 54
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    Neural processing letters 4 (1996), S. 45-52 
    ISSN: 1573-773X
    Schlagwort(e): analog neural network chip ; error back-propagation ; learning ; multilayer perceptron
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A modular analog neuro-chip with on-chip learning capability is described. Two popular learning algorithms, error back-propagation and Hebbian learning, are incorporated with adjustable learning parameters. This analog neuro-chip has a fully modular structure for easy multi-chip expansion. The numbers of synapses and neurons can be expanded by simple pin-to-pin connections without additional circuits. For effective learning, the learning rate, sigmoid slope, and ratio of Hebbian learning term to error back-propagation term can be controlled externally by digital signals. The chip is fabricated and successfully trained with gray-scale patterns as well as XOR problem.
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  • 55
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    Neural processing letters 7 (1998), S. 151-159 
    ISSN: 1573-773X
    Schlagwort(e): functional equation ; functional networks ; learning ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract In this letter we present functional networks. Unlike neural networks, in these networks there are no weightsassociated with the links connecting neurons, and the internal neuron functions are not fixed but learnable. These functions are not arbitrary, but subject to strong constraints to satisfy the compatibility conditions imposed by the existence of multiple links going from the last input layer to the same output units. In fact, writing the values of the output units in different forms, by considering these different links, a system of functional equations is obtained. When this system is solved, the numberof degrees of freedom of these initially multidimensional functions is considerably reduced. One example illustrates the process and shows that multidimensional functions can be reduced to functions with a single argument. To learn the resulting functions, a method based on minimizing a least squares error function is used, which, unlike the functions used in neural networks, has a single minimum.
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  • 56
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    Neural processing letters 8 (1998), S. 117-129 
    ISSN: 1573-773X
    Schlagwort(e): natural images ; Gabor wavelets ; Gestalt principles ; learning ; contextual information ; Banana wavelets
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract By investigating the second-order statistics of Gabor wavelet responses derived from natural images, we show that collinearity and parallelism are conspicuous relations. We give a precise mathematical characterization of these Gestalt principles by the conditional probability of two responses. Essential for our investigations is a non-linear transformation, initially utilized within the object recognition system [5], which transforms continuous Gabor wavelet responses into a binary code indicating the presence or absence of local oriented line segments.
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  • 57
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    Neural processing letters 4 (1996), S. 53-59 
    ISSN: 1573-773X
    Schlagwort(e): self-organizing feature map ; learning ; parameter ; lateral connection radius ; competition
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The behavior of self-organizing feature maps is critically dependent on parameters such as lateral connection radius, lateral inhibition intensity, and network size. With no theoretical guidelines for the choice of these parameters, they are usually selected through a trial-and-error process. In order to provide heuristic guidelines for future model designers as well as to give insight into which model features are responsible for specific aspects of maps, we systematically varied these parameters and studied their effects on the properties of a self-organizing feature map. The connectivity radius was found to determine the size of activation clusters quadratically. As the intensity of lateral inhibition was varied, feature patterns varied from stripe-like to clusters in the map, with other intermediate patterns also occurring. The number of clusters of each feature increased nonlinearly as the network size increased.
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  • 58
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    Neural processing letters 6 (1997), S. 1-10 
    ISSN: 1573-773X
    Schlagwort(e): auto-associator ; group theory ; learning ; modularity ; symmetry
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract In this paper, the auto-association problem is discussed using group theoretical methods. Considering the symmetry group of a given set of test sequences, it is shown to be possible to construct a class of neural networks acting as auto-associators on this set. It turns out that the symmetry of the network structure is already determined by the symmetries of the set of test sequences, indicating that learning a set of elements applied is concerned with finding invariant relations inherent in this set. Moreover, the main result offers the possibility, to construct all optimal network structures and, hence, to decide whether a solution found by a particular learning algorithm is optimal or not.
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  • 59
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    Neural processing letters 7 (1998), S. 1-4 
    ISSN: 1573-773X
    Schlagwort(e): classification problems ; generalization ; learning ; perceptrons ; sonar targets
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We study the classification of sonar targets first introduced by Gorman & Sejnowski (1988). We discovered that not only the training set and the test set of this benchmark are both linearly separable, although by different hyperplanes, but that the complete set of patterns, training and test patterns together, is also linearly separable. The distances of the patterns to the separating hyperplane determined by learning with the training set alone, and to the one determined by learning the complete data set, are presented.
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  • 60
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    Neural processing letters 8 (1998), S. 99-106 
    ISSN: 1573-773X
    Schlagwort(e): dynamical neural networks ; feedback control ; finite time convergence ; Lasalle theorem ; learning ; trajectory control
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We present a class of feedback control functions which increase the convergence rates of nonlinear dynamical systems. A simple sign function is used to obtain convergence in finite time. We describe a trajectory learning procedure which preserves the convergence property of the system. Based on the proposed feedback, we developed a new neural network model which converges in finite time.
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  • 61
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    Journal of systems integration 4 (1994), S. 171-184 
    ISSN: 1573-8787
    Schlagwort(e): Software science ; software work-effort estimation ; function points ; intelligent DSS ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Up to now, the assessment of work-effort in software engineering is based on statistical methods. Among the best known are COCOMO (Boehm [2]) or SPQR (Jones [6]). Nevertheless it is generally recognized that many qualitative factors enter into the cost of development, such as effectiveness of the team, user's motivation, and accuracy of the specifications. We have designed a Decision Support System (DSS) for estimating the work-effort, in which the processing of the qualitative data is made by an expert system while a function points analysis provides the theoretical work-effort according to the type of software and the past experience. The evaluation is performed at two levels: global and detailed. The global evaluation is made at the beginning of the development according to the data that are, at this moment, available. The detailed evaluation takes place when the design of the software becomes more precise. The software manager can follow the evolution of the changes at the detailed level during the development. In software development, project leaders mostly reason by using their past experience. It therefore follows that a DSS must contain a learning process. We have accordingly designed our system to record the data of the completed developments. These data serve for the new evaluations. At the end of each project, the learning module examines to what extent the already-recorded information must be updated. Thus our system combines statistic data and knowledge-based reasonings.
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  • 62
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    International journal of computer vision 38 (2000), S. 59-73 
    ISSN: 1573-1405
    Schlagwort(e): computer vision ; learning ; morphing ; action recognition ; nonrigid motion ; animation ; prototype ; linear superposition ; correspondence ; structural risk minimization
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The linear combination of prototypical views provides a powerful approach for the recognition and the synthesis of images of stationary three-dimensional objects. In this article, we present initial results that demonstrate that similar ideas can be developed for the recognition and synthesis of complex motion patterns. We present a technique that permits to represent complex motion or action patterns by linear combinations of a small number of prototypical image sequences. We demonstrate the applicability of this new approach for the synthesis and analysis of biological motion using simulated and real video data from different locomotion patterns. Our results show that complex motion patterns are embedded in pattern spaces with a defined topological structure, which can be uncovered with our methods. The underlying pattern space seems to have locally, but not globally, the properties of a linear vector space. We show how the knowledge about the topology of the pattern space can be exploited during pattern recognition. Our method may provide a new interesting approach for the analysis and synthesis of video sequences and complex movements.
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  • 63
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    Journal of computational neuroscience 3 (1996), S. 137-153 
    ISSN: 1573-6873
    Schlagwort(e): potassium ; compartmental ; learning ; plasticity ; simulation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Medizin , Physik
    Notizen: Abstract We developed a multicompartmental Hodgkin-Huxley model of the Hermissenda type-B photoreceptor and used it to address the relative contributions of reductions of two K+ currents, I a and I C, to changes in cellular excitability and synaptic strength that occur in these cells after associative learning. We found that reductions of gC, the peak conductance of I C, substantially increased the firing frequency of the type-B cell during the plateau phase of a simulated light response, whereas reductions of gA had only a modest contribution to the plateau frequency. This can be understood at least in part by the contributions of these currents to the light-induced (nonspiking) generator potential, the plateau of which was enhanced by gC reductions, but not by gA reductions. In contrast, however, reductions of gA broadened the type-B cell action potential, increased Ca2+ influx, and increased the size of the postsynaptic potential produced in a type-A cell, whereas similar reductions of gC had only negligible contributions to these measures. These results suggest that reductions of I A and I C play important but different roles in type-B cell plasticity.
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  • 64
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    Journal of computational neuroscience 3 (1996), S. 155-172 
    ISSN: 1573-6873
    Schlagwort(e): architecture ; lateral inhibition ; feedback inhibition ; learning ; plasticity
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Medizin , Physik
    Notizen: Abstract Because the Hermissenda eye is relatively simple and its cells well characterized, it provides an attractive preparation for detailed computational analysis. To examine the neural mechanisms of learning in this system, we developed multicompartmental models of the type-A and type-B photoreceptors, simulated the eye, and asked three questions: First, how do conductance changes affect cells in a network as compared with those in isolation; second, what are the relative contributions of increases in B-cell excitability and synaptic strength to network output; and third, how do these contributions vary as a function of network architecture? We found that reductions in the type-B cells of two K+ currents, I A and I C, differentially affected the type-B cells themselves, with I C reductions increasing firing rate (excitability) in response to light, and I A reductions increasing quantal output (synaptic strength) onto postsynaptic targets. Increases in either type-B cell excitability or synaptic strength, induced directly or indirectly, each suppressed A-cell photoresponses, and the combined effect of both changes occurring together was greater than either alone. To examine the effects of network architecture, we compared the full network with a simple feedforward B-A pair and intermediate configurations. Compared with a feedforward pair, the complete network exhibited greater A-cell sensitivity to B-cell changes. This was due to many factors, including an increased number of B-cells (which increased B-cell impact on A-cells), A-B feedback inhibition (which slowed both cell types and altered spike timing relationships), and B-B lateral inhibition (which reduced B-cell sensitivity to intrinsic biophysical modifications). These results suggest that an emergent property of the network is an increase both in the rate of information acquisition (“learning”) and in the amount of information that can be stored (“memory”).
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  • 65
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    Journal of computational neuroscience 5 (1998), S. 171-190 
    ISSN: 1573-6873
    Schlagwort(e): learning ; plasticity ; goldfish ; VOR ; cerebellum
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Medizin , Physik
    Notizen: Abstract Through the process of habituation, continued exposure to low-frequency (0.01 Hz) rotation in the dark produced suppression of the low-frequency response of the vestibulo-ocular reflex (VOR) in goldfish. The response did not decay gradually, as might be expected from an error-driven learning process, but displayed several nonlinear and nonstationary features. They included asymmetrical response suppression, magnitude-dependent suppression for lower- but not higher-magnitude head rotations, and abrupt-onset suppressions suggestive of a switching mechanism. Microinjection of lidocaine into the vestibulocerebellum of habituated goldfish resulted in a temporary dishabituation. This suggests that the vestibulocerebellum mediates habituation, presumably through Purkinje cell inhibition of vestibular nuclei neurons. The habituated VOR data were simulated with a feed-forward, nonlinear neural network model of the VOR in which only Purkinje cell inhibition of vestibular nuclei neurons was varied. The model suggests that Purkinje cell inhibition may switch in to introduce nonstationarities, and cause asymmetry and magnitude-dependency in the VOR to emerge from the essential nonlinearity of vestibular nuclei neurons.
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  • 66
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    Statistics and computing 2 (1992), S. 91-95 
    ISSN: 1573-1375
    Schlagwort(e): Causality ; induction ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above.
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  • 67
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    Journal of automated reasoning 18 (1997), S. 189-198 
    ISSN: 1573-0670
    Schlagwort(e): automated theorem proving ; competition ; DISCOUNT ; distributed theorem proving ; reactive planning ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract The DISCOUNT system is a distributed equational theorem prover based on the teamwork method for knowledge-based distribution. It uses an extended version of unfailing Knuth–Bendix completion that is able to deal with arbitrarily quantified goals. DISCOUNT features many different control strategies that cooperate using the teamwork approach. Competition between multiple strategies, combined with reactive planning, results in an adaptation of the whole system to given problems, and thus in a very high degree of independence from user interaction. Teamwork also provides a suitable framework for the use of control strategies based on learning from previous proof experiences. One of these strategies forms the core of the expert global_learn, which is capable of learning from successful proofs of several problems. This expert, running sequentially, was one of the entrants in the competition (DISCOUNT/GL), while a distributed DISCOUNT system running on two workstations was another en trant.
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  • 68
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    Journal of automated reasoning 4 (1988), S. 15-27 
    ISSN: 1573-0670
    Schlagwort(e): PRESS ; IMPRESS ; meta-level inference ; proof plans ; search control ; theorem proving ; algebra ; verification ; automatic programming ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We describe two uses of meta-level inference: to control the search for a proof; and to derive new control information, and illustrate them in the domain of algebraic equation solving. The derivation of control information is the main focus of the paper. It involves the proving of theorems in the Meta-Theory of Algebra. These proofs are guided by meta-meta-level inference. We are developing a meta-meta-language to describe formulae, and proof plans, and have built a program, IMPRESS, which uses these plans to build a proof. We describe one such proof plan in detail. IMPRESS will form part of a self-improving algebra system.
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  • 69
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    Computational optimization and applications 14 (1999), S. 87-102 
    ISSN: 1573-2894
    Schlagwort(e): noncooperative games ; Nash equilibrium ; learning ; projected subgradient ; stochastic approximation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We model repeated play of noncooperative stage games in terms of approximate gradient steps. That simple format requires little information and no optimization. Moreover, it allows players to evaluate marginal cost or profit inexactly and to move with different velocities. Uncertainty can also be accommodated. Granted some crucial stability, we show that play converges to Nash equilibrium.
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    User modeling and user adapted interaction 10 (2000), S. 181-208 
    ISSN: 1573-1391
    Schlagwort(e): agent ; cluster analysis ; data mining ; instructional technology ; instrumentation ; knowledge acquisition ; learning ; logging ; long-term observation ; organizational learning ; OWL ; recommender system ; sequence analysis ; student modeling
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Information technology has recently become the medium in which much professional office work is performed. This change offers an unprecedented opportunity to observe and record exactly how that work is performed. We describe our observation and logging processes and present an overview of the results of our long-term observations of a number of users of one desktop application. We then present our method of providing individualized instruction to each user by employing a new kind of user model and a new kind of expert model. The user model is based on observing the individual's behavior in a natural environment, while the expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual's knowledge to the pooled knowledge of her peers.
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  • 71
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    User modeling and user adapted interaction 3 (1993), S. 107-153 
    ISSN: 1573-1391
    Schlagwort(e): information processing ; learning ; knowledge representation ; CAI ; ICAI ; AI
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract In the present work, the semantics of the Extended Genetic Graph (EGG) is defined in order to eliminate limitations inherent in these graphs in the modelling of an ideal Student Model. The semantics of extended genetic graphs can be defined at two representational levels: conceptual and transactional. First, the student's knowledge as represented by EGG nodes is specified explicitly at the conceptual level using the conceptual graphs (CGs) as a representation. Secondly, the criteria for the definition and use of learning processes such asanalogy, generalization, refinement, component, anddeviation/correction are specified at the transactional level. These criteria are then associated with the conditions of existence of different EGG links as they are implicitly assumed in the semantics of these graphs. Once the conditions of their creation are known, the semantics of EGG links can be represented explicitly by the use of CGs and Predicate Transition Networks (PrTNs). These representations are then used for detecting different types of EGG links. Conceptual graphs combined with PrTNs are able to describe the semantic structures equivalent to those contained implicitly in EGGs. However, the semantics of the combined graph which is based on the results of cognitive psychology, natural language processing, as well as logic, are richer than the semantics of the EGG. Furthermore, the operations provided by the conceptual graph theory combined with the constraint specifications as expressed by PrTNs allow the modification of the learner graph. Thus, our proposed representational framework provides the basis for the construction of a deep dynamical student model. An example from the Boolean Algebra domain demonstrates its feasibility.
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  • 72
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    User modeling and user adapted interaction 4 (1994), S. 253-278 
    ISSN: 1573-1391
    Schlagwort(e): Student modeling ; learning ; empirical validity ; procedural knowledge ; intelligent tutoring systems ; mastery learning ; individual differences
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.
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  • 73
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    International journal of computer vision 36 (2000), S. 171-193 
    ISSN: 1573-1405
    Schlagwort(e): optical flow ; motion discontinuities ; occlusion ; steerable filters ; learning ; eigenspace methods ; motion-based recognition ; non-rigid and articulated motion
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic “motion features” such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.
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  • 74
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    International journal of computer vision 38 (2000), S. 75-91 
    ISSN: 1573-1405
    Schlagwort(e): computer vision ; learning ; correspondence ; morphable models ; surface matching
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract We describe a novel automatic technique for finding a dense correspondence between a pair of n-dimensional surfaces with arbitrary topologies. This method employs a different formulation than previous correspondence algorithms (such as optical flow) and includes images as a special case. We use this correspondence algorithm to build Morphable Surface Models (an extension of Morphable Models) from examples. We present a method for matching the model to new surfaces and demonstrate their use for analysis, synthesis, and clustering.
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  • 75
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    International journal of computer vision 38 (2000), S. 5-7 
    ISSN: 1573-1405
    Schlagwort(e): machine vision ; learning ; regularization ; graphics
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This special issue is dedicated to some of the recent work at the Center for Biological and Computational Learning at MIT in applying machine learning techniques to computer vision.
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  • 76
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    Autonomous agents and multi-agent systems 2 (1999), S. 173-207 
    ISSN: 1573-7454
    Schlagwort(e): multi-agent systems ; coordination ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract Achieving effective cooperation in a multi-agent system is a difficult problem for a number of reasons such as limited and possibly out-dated views of activities of other agents and uncertainty about the outcomes of interacting non-local tasks. In this paper, we present a learning system called COLLAGE, that endows the agents with the capability to learn how to choose the most appropriate coordination strategy from a set of available coordination strategies. COLLAGE relies on meta-level information about agents' problem solving situations to guide them towards a suitable choice for a coordination strategy. We present empirical results that strongly indicate the effectiveness of the learning algorithm.
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  • 77
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    Applied intelligence 7 (1997), S. 79-90 
    ISSN: 1573-7497
    Schlagwort(e): case-based reasoning ; learning ; medical expert systems
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper introduces ChartD 2, a Congenital Heart Disease Diagnostician that employs a case-based model where specific and general knowledge are combined in reasoning. Specific knowledge is represented in the form of cases while general knowledge is represented in the form of category descriptors. When solving a new case, ChartD 2 uses its general knowledge to draw hypotheses and to guide the search for the most similar cases it has already “seen”. The retrieved cases, representing specific knowledge, are then used to support one of the hypotheses and to justify the conclusion reached. ChartD 2 has been based on an earlier hybrid connectionist/symbolic program called Hycones, developed in the same application domain. Besides enhancing some of Hycones' capabilities, the new system proposes solutions for common problems in Case-Based Reasoning (CBR), such as case matching, indexing and learning. The system ChartD 2 is presented and evaluated, using real cases collected from a medical database. The performance of the system is contrasted with that of Hycones and two other learning algorithms. Moreover, similar research efforts on the use of other sources of knowledge by CBR systems are discussed, and topics for further research are suggested.
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    Autonomous robots 7 (1999), S. 89-113 
    ISSN: 1573-7527
    Schlagwort(e): learning ; evolution ; plastic individuals ; Baldwin Effect
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In the last few years several researchers have resorted to artificial evolution (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two different purposes: (a) looking at the performance advantages obtained by combining these two adaptive techniques; (b) understanding the role of the interaction between learning and evolution in biological organisms. In this paper we describe some of the most representative experiments conducted in this area and point out their implications for both perspectives outlined above. Understanding the interaction between learning and evolution is probably one of the best examples in which computational studies have shed light on problems that are difficult to study with the research tools employed by evolutionary biology and biology in general. From an engineering point of view, the most relevant results are those showing that adaptation in dynamic environments gains a significant advantage by the combination of evolution and learning. These studies also show that the interaction between learning and evolution deeply alters the evolutionary and the learning process themselves, offering new perspectives from a biological point of view. The study of learning within an evolutionary perspective is still in its infancy and in the forthcoming years it will produce an enormous impact on our understanding of how learning and evolution operate.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 79
    Digitale Medien
    Digitale Medien
    Springer
    Autonomous robots 8 (2000), S. 269-292 
    ISSN: 1573-7527
    Schlagwort(e): cooperative robotics ; heterogeneous systems ; symbolic communication ; symbol grounding ; learning ; representation ; behavior-based ; mobile robots
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In this paper, we describe the implementation of a heterogeneous cooperative multi-robot system that was designed with a goal of engineering a grounded symbolic representation in a bottom-up fashion. The system comprises two autonomous mobile robots that perform cooperative cleaning. Experiments demonstrate successful purposive navigation, map building and the symbolic communication of locations in a behavior-based system. We also examine the perceived shortcomings of the system in detail and attempt to understand them in terms of contemporary knowledge of human representation and symbolic communication. From this understanding, we propose the Adaptive Symbol Grounding Hypothesis as a conception for how symbolic systems can be envisioned.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 80
    Digitale Medien
    Digitale Medien
    Springer
    Autonomous robots 9 (2000), S. 51-58 
    ISSN: 1573-7527
    Schlagwort(e): deformable object manipulation ; learning ; iterative lifting
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper addresses the problem of robotic grasping and manipulation of 3-D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. Our methodology relies on the implementation of two main tasks. Our first task is to calculate deformation characteristics for a non-rigid object represented by a physically-based model. Using nonlinear partial differential equations, we model the particle motion of the deformable object in order to calculate the deformation characteristics. For our second task, we must calculate the minimum force required to successfully lift the deformable object. This minimum lifting force can be learned using a technique called ‘iterative lifting’. Once the deformation characteristics and the associated lifting force term are determined, they are used to train a neural network for extracting the minimum force required for subsequent deformable object manipulation tasks. Our developed algorithm is validated with two sets of experiments. The first experimental results are derived from the implementation of the algorithm in a simulated environment. The second set involves a physical implementation of the technique whose outcome is compared with the simulation results to test the real world validity of the developed methodology.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 81
    Digitale Medien
    Digitale Medien
    Springer
    Computer supported cooperative work 7 (1998), S. 47-86 
    ISSN: 1573-7551
    Schlagwort(e): Collaboration ; learning ; CSCL ; MUDs ; community ; constructionism
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract MOOSE Crossing is a text-based virtual reality environment (or “MUD”) designed to be a constructionist learning environment for children ages eight to thirteen. The constructionist philosophy of education argues that learning through designing and constructing personally meaningful projects is better than learning by being told. Children on MOOSE Crossing learn computer programming and improve their reading and writing by working on self-selected projects in a self-motivated, peer-supported fashion. In experience with over 180 children and 90 adults using the system since October 1995, we have found that the community provides essential support for the children's learning experiences. The community provides role models; situated, ubiquitous project models; emotional support to overcome technophobia; technical support; and an appreciative audience for completed work. This paper examines the nature of that support in detail, and argues that community support for learning is an essential element in collaborative work and learning on the Internet.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 82
    Digitale Medien
    Digitale Medien
    Springer
    Data mining and knowledge discovery 1 (1997), S. 79-119 
    ISSN: 1573-756X
    Schlagwort(e): Bayesian networks ; Bayesian statistics ; learning ; missing data ; classification ; regression ; clustering ; causal discovery
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A Bayesian network is a graphical model that encodesprobabilistic relationships among variables of interest. When used inconjunction with statistical techniques, the graphical model hasseveral advantages for data modeling. One, because the model encodesdependencies among all variables, it readily handles situations wheresome data entries are missing. Two, a Bayesian network can be used tolearn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequencesof intervention. Three, because the model has both a causal andprobabilistic semantics, it is an ideal representation for combiningprior knowledge (which often comes in causal form) and data. Four,Bayesian statistical methods in conjunction with Bayesian networksoffer an efficient and principled approach for avoiding theoverfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarizeBayesian statistical methods for using data to improve these models.With regard to the latter task, we describe methods for learning boththe parameters and structure of a Bayesian network, includingtechniques for learning with incomplete data. In addition, we relateBayesian-network methods for learning to techniques for supervised andunsupervised learning. We illustrate the graphical-modeling approachusing a real-world case study.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 83
    Digitale Medien
    Digitale Medien
    Springer
    International journal of parallel programming 6 (1977), S. 9-25 
    ISSN: 1573-7640
    Schlagwort(e): Semantic memory net ; learning ; natural language processing ; mechanical translation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract A central problem of many branches of artificial intelligence (AI) research is that ofunderstanding natural language (NL). Many attempts have been made to model understanding with computer systems that demonstrate competence at such tasks as question answering, paraphrasing, and following commands. The system to be described in this paper combines some of these language functions in a single, general process based on the creation of an associative memory net as a result of experience. The author has written a large, interactive computer program that accepts unsegmented input strings of natural language from a human trainer and, after processing each string, outputs a natural language response. The processing of the string may involve transforming it to some other form in the same or another language, or answering an input question based on information previously learned by the program.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 84
    Digitale Medien
    Digitale Medien
    Springer
    Annals of mathematics and artificial intelligence 2 (1990), S. 209-220 
    ISSN: 1573-7470
    Schlagwort(e): Problem-solving ; state-space ; heuristic search ; heuristic evaluation function ; Bayesian probability ; decision theory ; utility theory ; learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: Abstract Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as “criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal”. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality. To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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