Martin V. Butz – författare

More than 2000 years ago Greek philosophers were pondering the puzzling dichotomy between our physical bodies and our seemingly non-physical minds. Yet even today, it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. How is it that we can think highly abstract thoughts, seemingly fully detached from the actual, physical reality? This book offers an interdisciplinary introduction to embodied cognitive science, addressing the question of how the mind comes into being while actively interacting with and learning from the environment by means of the own body. By pursuing a functional and computational perspective, concrete answers are provided about the fundamental mechanisms and developing structures that must bring the mind about, taking into account insights from biology, neuroscience, psychology, and philosophy as well as from computer science, machine learning, and artificial intelligence. The book provides introductions to the most important challenges and available computational approaches on how the mind comes into being. The book includes exercises, helping the reader to grasp the material and understand it in a broader context. References to further studies, methodological details, and current developments support more advanced studies beyond the covered material. While the book is written in advanced textbook style with the primary target group being undergraduates in cognitive science and related disciplines, readers with a basic scientific background and a strong interest in how the mind works will find this book intriguing and revealing.

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

This book offers a comprehensive introduction to learning classifier systems (LCS) ? or more generally, rule-based evolutionary online learning systems. LCSs learn interactively ? much like a neural network ? but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system ? the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data- mining problems. Reconsidering John Holland?s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.

The matter of anticipation is, as the editors of this volume state in their preface, a rathernewtopic. Giventhealmostconstantusewemakeofanticipationin our dailyliving,itseemsoddthatthebulk ofpsychologistshavepersistentlyignored it. However, the reason for this disregard is not di?cult to ?nd. The dogma of the scienti?c revolution had from the outset laid down the principle that future conditions and events could not in?uence the present. The law of causation clearly demands that causes should precedetheir e?ects and, therefore,concepts such as purpose, anticipation, and even intention were taboo because they were thought to involve things and happenings that lay ahead in time. An analysis of the three concepts - purpose, anticipation, and intention - shows that they are rooted in the past and transcend the present only insofar as they contain mental representations of things to be striven for or avoided. Purposiveorgoal-directedactioncouldbe circumscribedasactioncarriedoutto attain something desirable. In each case, the particular action is chosenbecause, in the past, it has more or less reliably led to the desired end.The only way the future is involved in this procedure is through the belief that the experiential worldmanifestssomeregularityandallowsthelivingorganismtoanticipatethat what has workedin the past will continue to work in the future. This belief does not have to be conscious. Skinner's rats continued to turn left in a maze where theleftarmhadbeenbaited. Theydidsobecausethemeatpelletthey foundthe ?rst time had "reinforced" them to repeat the turn to the left.

Anticipatory behavior in adaptive learning systems is steadily gaining the - terest of scientists, although many researchers still do not explicitly consider the actual anticipatory capabilities of their systems. Similarly to the previous two workshops, the third workshop on anticipatory behavior in adaptive lea- ing systems (ABiALS 2006) has shown yet again that the similarities between di?erent anticipatory mechanisms in diverse cognitive systems are striking. The discussions and presentations on the workshop day of September 30th, 2006, during the Simulation of Adaptive Behavior Conference (SAB 2006), con?rmed that the investigations into anticipatory cognitive mechanisms for behavior and learning strongly overlap among researchers from various disciplines, including the whole interdisciplinary cognitive science area. Thus, further conceptualizations of anticipatory mechanisms seem man- tory. The introductory chapter of this volume therefore does not only provide an overview of the contributions included in this volume but also proposes a taxonomy of how anticipatory mechanisms can improve adaptive behavior and learning in cognitive systems. During the workshop it became clear that ant- ipations are involved in various cognitive processes that range from individual anticipatory mechanisms to social anticipatory behavior. This book re?ects this structure by ?rst providing neuroscienti?c as well as psychological evidence for anticipatorymechanismsinvolvedinbehavior,learning,language,andcognition. Next,individualpredictivecapabilitiesandanticipatorybehaviorcapabilitiesare investigated. Finally, anticipation relevant in social interaction is studied.

The general idea that brains anticipate the future, that they engage in prediction, and that one means of doing this is through some sort of inner model that can be run of?ine,hasalonghistory. SomeversionoftheideawascommontoAristotle,aswell as to many medieval scholastics, to Leibniz and Hume, and in more recent times, to Kenneth Craik and Philip Johnson-Laird. One reason that this general idea recurs continually is that this is the kind of picture that introspection paints. When we are engaged in tasks it seems that we form images that are predictions, or anticipations, and that these images are isomorphic to what they represent. But as much as the general idea recurs, opposition to it also recurs. The idea has never been widely accepted, or uncontroversial among psychologists, cognitive scientists and neuroscientists. The main reason has been that science cannot be s- is?ed with metaphors and introspection. In order to gain acceptance, an idea needs to be formulated clearly enough so that it can be used to construct testable hypot- ses whose results will clearly supportor cast doubtupon the hypothesis. Next, those ideasthatare formulablein one oranothersortof symbolismor notationare capable of being modeled, and modeling is a huge part of cognitive neuroscience. If an idea cannot be clearly modeled, then there are limits to how widely it can be tested and accepted by a cognitive neuroscience community.

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Anticipatory behavior in adaptive learning systems continues attracting attention of researchers in many areas, including cognitive systems, neuroscience, psychology, and machine learning. This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2008, held in Munich, Germany, in June 2008, in collaboration with the six-monthly Meeting of euCognition 'The Role of Anticipation in Cognition'. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The introductory chapter of this state-of-the-art survey not only provides an overview of the contributions included in this volume but also revisits the current available terminology on anticipatory behavior and relates it to the available system approaches. The papers are organized in topical sections on anticipation in psychology with focus on the ideomotor view, conceptualizations, anticipation and dynamical systems, computational modeling of psychological processes in the individual and social domains, behavioral and cognitive capabilities based on anticipation, and computational frameworks and algorithms for anticipation, and their evaluation.

This book offers a comprehensive introduction to learning classifier systems (LCS) -- or more generally, rule-based evolutionary online learning systems. LCSs learn interactively -- much like a neural network -- but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system -- the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland's original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.