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  • 1
    Online Resource
    Online Resource
    Berlin, Heidelberg : Springer Berlin Heidelberg
    UID:
    b3kat_BV041889707
    Format: 1 Online-Ressource
    ISBN: 9783642051814
    Series Statement: Studies in Computational Intelligence 264
    Note: From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop "From motor to interaction learning in robots" held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium , From Motor Learning to Interaction Learning in Robots -- From Motor Learning to Interaction Learning in Robots -- I: Biologically Inspired Models for Motor Learning -- Distributed Adaptive Control: A Proposal on the Neuronal Organization of Adaptive Goal Oriented Behavior -- Proprioception and Imitation: On the Road to Agent Individuation -- Adaptive Optimal Feedback Control with Learned Internal Dynamics Models -- The SURE_REACH Model for Motor Learning and Control of a Redundant Arm: From Modeling Human Behavior to Applications in Robotics -- Intrinsically Motivated Exploration for Developmental and Active Sensorimotor Learning -- II: Learning Policies for Motor Control -- Learning to Exploit Proximal Force Sensing: A Comparison Approach -- Learning Forward Models for the Operational Space Control of Redundant Robots -- Real-Time Local GP Model Learning -- Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling -- A Bayesian View on Motor Control and Planning -- Methods for Learning Control Policies from Variable-Constraint Demonstrations -- Motor Learning at Intermediate Reynolds Number: Experiments with Policy Gradient on the Flapping Flight of a Rigid Wing -- III: Imitation and Interaction Learning -- Abstraction Levels for Robotic Imitation: Overview and Computational Approaches -- Learning to Imitate Human Actions through Eigenposes -- Incremental Learning of Full Body Motion Primitives -- Can We Learn Finite State Machine Robot Controllers from Interactive Demonstration? -- Mobile Robot Motion Control from Demonstration and Corrective Feedback -- Learning Continuous Grasp Affordances by Sensorimotor Exploration -- Multimodal Language Acquisition Based on Motor Learning and Interaction -- Human-Robot Cooperation Based on Interaction Learning
    Additional Edition: Erscheint auch als Druckausgabe ISBN 978-3-642-05180-7
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Robotik ; Maschinelles Lernen ; Motorisches Lernen ; Mensch-Maschine-Kommunikation ; Nachahmung ; Interaktion ; Konferenzschrift
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  • 2
    Book
    Book
    Berlin ; Heidelberg :Springer,
    UID:
    almahu_BV036090214
    Format: XI, 538 S. : , Ill., graph. Darst. ; , 24 cm.
    ISBN: 978-3-642-05180-7
    Series Statement: Studies in computational intelligence 264
    Note: Literaturangaben
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Robotik ; Maschinelles Lernen ; Motorisches Lernen ; Mensch-Maschine-Kommunikation ; Nachahmung ; Interaktion ; Konferenzschrift
    Author information: Sigaud, Olivier.
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  • 3
    UID:
    almafu_9959328958402883
    Format: 1 online resource (481 pages)
    ISBN: 9781118557426 , 1118557425 , 9781118619872 , 1118619870
    Series Statement: ISTE
    Uniform Title: Processus décisionnels de Markov en intelligence artificielle. English.
    Content: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrations.
    Note: 3.5. Towards an analysis of dynamic programming in Lp-norm. , 2.2.4. General preliminaries on estimation methods2.3. Monte Carlo methods; 2.4. From Monte Carlo to temporal difference methods; 2.5. Temporal difference methods; 2.5.1. The TD(0) algorithm; 2.5.2. The SARSA algorithm; 2.5.3. The Q-learning algorithm; 2.5.4. The TD, SARSA and Q algorithms; 2.5.5. Eligibility traces and TD; 2.5.6. From TD to SARSA; 2.5.7. Q; 2.5.8. The R-learning algorithm; 2.6. Model-based methods: learning a model; 2.6.1. Dyna architectures; 2.6.2. The E3 algorithm; 2.6.3. The Rmax algorithm; 2.7. Conclusion; 2.8. Bibliography. , Chapter 3. Approximate Dynamic Programming3.1. Introduction; 3.2. Approximate value iteration (AVI); 3.2.1. Sample-based implementation and supervised learning; 3.2.2. Analysis of the AVI algorithm; 3.2.3. Numerical illustration; 3.3. Approximate policy iteration (API); 3.3.1. Analysis in L [infinity symbol]-norm of the API algorithm; 3.3.2. Approximate policy evaluation; 3.3.3. Linear approximation and least-squares methods; 3.3.3.1. TD; 3.3.3.2. Least-squares methods; 3.3.3.3. Linear approximation of the state-action value function; 3.4. Direct minimization of the Bellman residual. , Cover; Title Page; Copyright Page; Table of Contents; Preface; List of Authors; PART 1. MDPS: MODELS AND METHODS; Chapter 1. Markov Decision Processes; 1.1. Introduction; 1.2. Markov decision problems; 1.2.1. Markov decision processes; 1.2.2. Action policies; 1.2.3. Performance criterion; 1.3. Value functions; 1.3.1. The finite criterion; 1.3.2. The [beta]-discounted criterion; 1.3.3. The total reward criterion; 1.3.4. The average reward criterion; 1.4. Markov policies; 1.4.1. Equivalence of history-dependent and Markov policies; 1.4.2. Markov policies and valued Markov chains. , 1.5. Characterization of optimal policies1.5.1. The finite criterion; 1.5.1.1. Optimality equations; 1.5.1.2. Evaluation of a deterministic Markov policy; 1.5.2. The discounted criterion; 1.5.2.1. Evaluation of a stationary Markov policy; 1.5.2.2. Optimality equations; 1.5.3. The total reward criterion; 1.5.4. The average reward criterion; 1.5.4.1. Evaluation of a stationary Markov policy; 1.5.4.2. Optimality equations; 1.6. Optimization algorithms for MDPs; 1.6.1. The finite criterion; 1.6.2. The discounted criterion; 1.6.2.1. Linear programming; 1.6.2.2. The value iteration algorithm. , 1.6.2.3. The policy iteration algorithm1.6.3. The total reward criterion; 1.6.3.1. Positive MDPs; 1.6.3.2. Negative MDPs; 1.6.4. The average criterion; 1.6.4.1. Relative value iteration algorithm; 1.6.4.2. Modified policy iteration algorithm; 1.7. Conclusion and outlook; 1.8. Bibliography; Chapter 2. Reinforcement Learning; 2.1. Introduction; 2.1.1. Historical overview; 2.2. Reinforcement learning: a global view; 2.2.1. Reinforcement learning as approximate dynamic programming; 2.2.2. Temporal, non-supervised and trial-and-error based learning; 2.2.3. Exploration versus exploitation.
    Additional Edition: Print version: Sigaud, Olivier. Markov Decision Processes in Artificial Intelligence. London : Wiley, ©2013 ISBN 9781848211674
    Language: English
    Keywords: Electronic books. ; Electronic books. ; Electronic books.
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  • 4
    UID:
    almahu_9948621630602882
    Format: X, 303 p. , online resource.
    Edition: 1st ed. 2003.
    ISBN: 9783540450023
    Series Statement: Lecture Notes in Artificial Intelligence ; 2684
    Note: Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior -- Philosophical Considerations -- Whose Anticipations? -- Not Everything We Know We Learned -- From Cognitive Psychology to Cognitive Systems -- Anticipatory Behavioral Control -- Towards a Four Factor Theory of Anticipatory Learning -- Formulations, Distinctions, and Characteristics -- Internal Models and Anticipations in Adaptive Learning Systems -- Mathematical Foundations of Discrete and Functional Systems with Strong and Weak Anticipations -- Anticipation Driven Artificial Personality: Building on Lewin and Loehlin -- A Framework for Preventive State Anticipation -- Symbols and Dynamics in Embodied Cognition: Revisiting a Robot Experiment -- Systems, Evaluations, and Applications -- Forward and Bidirectional Planning Based on Reinforcement Learning and Neural Networks in a Simulated Robot -- Sensory Anticipation for Autonomous Selection of Robot Landmarks -- Representing Robot-Environment Interactions by Dynamical Features of Neuro-controllers -- Anticipatory Guidance of Plot -- Exploring the Value of Prediction in an Artificial Stock Market -- Generalized State Values in an Anticipatory Learning Classifier System.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783540404293
    Additional Edition: Printed edition: ISBN 9783662204177
    Language: English
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  • 5
    UID:
    gbv_605496358
    Format: Online-Ressource (XI, 333 S.)
    Edition: Online-Ausg. 2009 Springer eBook collection. Computer science Electronic reproduction; Available via World Wide Web
    ISBN: 9783642025655
    Series Statement: Lecture notes in computer science 5499
    Note: Literaturangaben , Electronic reproduction; Available via World Wide Web
    Additional Edition: ISBN 3642025641
    Additional Edition: ISBN 9783642025648
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Lernendes System ; Adaptives System ; Antizipation ; Kognitive Psychologie ; Lernendes System ; Adaptives System ; Antizipation ; Kognitive Psychologie ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
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  • 6
    UID:
    almahu_9947364195302882
    Format: X, 382 p. , online resource.
    ISBN: 9783540742623
    Series Statement: Lecture Notes in Computer Science, 4520
    Content: 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.
    Note: Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems -- Anticipatory Aspects in Brains, Language, and Cognition -- Neural Correlates of Anticipation in Cerebellum, Basal Ganglia, and Hippocampus -- The Role of Anticipation in the Emergence of Language -- Superstition in the Machine -- Individual Anticipatory Frameworks -- From Actions to Goals and Vice-Versa: Theoretical Analysis and Models of the Ideomotor Principle and TOTE -- Project “Animat Brain”: Designing the Animat Control System on the Basis of the Functional Systems Theory -- Cognitively Inspired Anticipatory Adaptation and Associated Learning Mechanisms for Autonomous Agents -- Schema-Based Design and the AKIRA Schema Language: An Overview -- Learning Predictions and Anticipations -- Training and Application of a Visual Forward Model for a Robot Camera Head -- A Distributed Computational Model of Spatial Memory Anticipation During a Visual Search Task -- A Testbed for Neural-Network Models Capable of Integrating Information in Time -- Construction of an Internal Predictive Model by Event Anticipation -- Anticipatory Individual Behavior -- The Interplay of Analogy-Making with Active Vision and Motor Control in Anticipatory Robots -- An Intrinsic Neuromodulation Model for Realizing Anticipatory Behavior in Reaching Movement under Unexperienced Force Fields -- Anticipating Rewards in Continuous Time and Space: A Case Study in Developmental Robotics -- Anticipatory Model of Musical Style Imitation Using Collaborative and Competitive Reinforcement Learning -- Anticipatory Social Behavior -- An Anticipatory Trust Model for Open Distributed Systems -- Anticipatory Alignment Mechanisms for Behavioral Learning in Multi Agent Systems -- Backward vs. Forward-Oriented Decision Making in the Iterated Prisoner’s Dilemma: A Comparison Between Two Connectionist Models -- An Experimental Study of Anticipation in Simple Robot Navigation.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540742616
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    UID:
    almahu_9948197955802882
    Format: 1 online resource (481 pages)
    ISBN: 9781118557426 , 1118557425 , 9781118619872 , 1118619870
    Series Statement: ISTE
    Uniform Title: Processus décisionnels de Markov en intelligence artificielle. English.
    Content: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrations.
    Note: 3.5. Towards an analysis of dynamic programming in Lp-norm. , 2.2.4. General preliminaries on estimation methods2.3. Monte Carlo methods; 2.4. From Monte Carlo to temporal difference methods; 2.5. Temporal difference methods; 2.5.1. The TD(0) algorithm; 2.5.2. The SARSA algorithm; 2.5.3. The Q-learning algorithm; 2.5.4. The TD, SARSA and Q algorithms; 2.5.5. Eligibility traces and TD; 2.5.6. From TD to SARSA; 2.5.7. Q; 2.5.8. The R-learning algorithm; 2.6. Model-based methods: learning a model; 2.6.1. Dyna architectures; 2.6.2. The E3 algorithm; 2.6.3. The Rmax algorithm; 2.7. Conclusion; 2.8. Bibliography. , Chapter 3. Approximate Dynamic Programming3.1. Introduction; 3.2. Approximate value iteration (AVI); 3.2.1. Sample-based implementation and supervised learning; 3.2.2. Analysis of the AVI algorithm; 3.2.3. Numerical illustration; 3.3. Approximate policy iteration (API); 3.3.1. Analysis in L [infinity symbol]-norm of the API algorithm; 3.3.2. Approximate policy evaluation; 3.3.3. Linear approximation and least-squares methods; 3.3.3.1. TD; 3.3.3.2. Least-squares methods; 3.3.3.3. Linear approximation of the state-action value function; 3.4. Direct minimization of the Bellman residual. , Cover; Title Page; Copyright Page; Table of Contents; Preface; List of Authors; PART 1. MDPS: MODELS AND METHODS; Chapter 1. Markov Decision Processes; 1.1. Introduction; 1.2. Markov decision problems; 1.2.1. Markov decision processes; 1.2.2. Action policies; 1.2.3. Performance criterion; 1.3. Value functions; 1.3.1. The finite criterion; 1.3.2. The [beta]-discounted criterion; 1.3.3. The total reward criterion; 1.3.4. The average reward criterion; 1.4. Markov policies; 1.4.1. Equivalence of history-dependent and Markov policies; 1.4.2. Markov policies and valued Markov chains. , 1.5. Characterization of optimal policies1.5.1. The finite criterion; 1.5.1.1. Optimality equations; 1.5.1.2. Evaluation of a deterministic Markov policy; 1.5.2. The discounted criterion; 1.5.2.1. Evaluation of a stationary Markov policy; 1.5.2.2. Optimality equations; 1.5.3. The total reward criterion; 1.5.4. The average reward criterion; 1.5.4.1. Evaluation of a stationary Markov policy; 1.5.4.2. Optimality equations; 1.6. Optimization algorithms for MDPs; 1.6.1. The finite criterion; 1.6.2. The discounted criterion; 1.6.2.1. Linear programming; 1.6.2.2. The value iteration algorithm. , 1.6.2.3. The policy iteration algorithm1.6.3. The total reward criterion; 1.6.3.1. Positive MDPs; 1.6.3.2. Negative MDPs; 1.6.4. The average criterion; 1.6.4.1. Relative value iteration algorithm; 1.6.4.2. Modified policy iteration algorithm; 1.7. Conclusion and outlook; 1.8. Bibliography; Chapter 2. Reinforcement Learning; 2.1. Introduction; 2.1.1. Historical overview; 2.2. Reinforcement learning: a global view; 2.2.1. Reinforcement learning as approximate dynamic programming; 2.2.2. Temporal, non-supervised and trial-and-error based learning; 2.2.3. Exploration versus exploitation.
    Additional Edition: Print version: Sigaud, Olivier. Markov Decision Processes in Artificial Intelligence. London : Wiley, ©2013 ISBN 9781848211674
    Language: English
    Keywords: Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    UID:
    almahu_9947364157802882
    Format: XI, 335 p. , online resource.
    ISBN: 9783642025655
    Series Statement: Lecture Notes in Computer Science, 5499
    Content: Anticipatory behavior in adaptive learning systems continues to attract the 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 5th 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 discusses the current various terminology employed in the field and relates it to the various system approaches. The papers are organized in topical sections on anticipation in psychology with a focus on the ideomotor view; theoretical and review contributions; 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.
    Note: From Sensorimotor to Higher-Level Cognitive Processes: An Introduction to Anticipatory Behavior Systems -- Anticipation in Psychology: Focus on the Ideomotor View -- ABC: A Psychological Theory of Anticipative Behavioral Control -- Anticipative Control of Voluntary Action: Towards a Computational Model -- Theoretical and Review Contributions -- Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes -- Steps to a Cyber-Physical Model of Networked Embodied Anticipatory Behavior -- Neural Pathways of Embodied Simulation -- Anticipation and Dynamical Systems -- The Autopoietic Nature of the “Inner World” -- The Cognitive Body: From Dynamic Modulation to Anticipation -- Computational Modelling of Psychological Processes in the Individual and Social Domains -- A Neurocomputational Model of Anticipation and Sustained Inattentional Blindness in Hierarchies -- Anticipation of Time Spans: New Data from the Foreperiod Paradigm and the Adaptation of a Computational Model -- Collision-Avoidance Characteristics of Grasping -- The Role of Anticipation on Cooperation and Coordination in Simulated Prisoner’s Dilemma Game Playing -- Behavioral and Cognitive Capabilities Based on Anticipation -- A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion -- Space Perception through Visuokinesthetic Prediction -- Anticipatory Driving for a Robot-Car Based on Supervised Learning -- Computational Frameworks and Algorithms for Anticipation, and Their Evaluation -- Prediction Time in Anticipatory Systems -- Multiscale Anticipatory Behavior by Hierarchical Reinforcement Learning -- Anticipatory Learning Classifier Systems and Factored Reinforcement Learning.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783642025648
    Language: English
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  • 9
    UID:
    almahu_9947920694302882
    Format: X, 303 p. , online resource.
    ISBN: 9783540450023
    Series Statement: Lecture Notes in Computer Science, 2684
    Note: Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior -- Philosophical Considerations -- Whose Anticipations? -- Not Everything We Know We Learned -- From Cognitive Psychology to Cognitive Systems -- Anticipatory Behavioral Control -- Towards a Four Factor Theory of Anticipatory Learning -- Formulations, Distinctions, and Characteristics -- Internal Models and Anticipations in Adaptive Learning Systems -- Mathematical Foundations of Discrete and Functional Systems with Strong and Weak Anticipations -- Anticipation Driven Artificial Personality: Building on Lewin and Loehlin -- A Framework for Preventive State Anticipation -- Symbols and Dynamics in Embodied Cognition: Revisiting a Robot Experiment -- Systems, Evaluations, and Applications -- Forward and Bidirectional Planning Based on Reinforcement Learning and Neural Networks in a Simulated Robot -- Sensory Anticipation for Autonomous Selection of Robot Landmarks -- Representing Robot-Environment Interactions by Dynamical Features of Neuro-controllers -- Anticipatory Guidance of Plot -- Exploring the Value of Prediction in an Artificial Stock Market -- Generalized State Values in an Anticipatory Learning Classifier System.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540404293
    Language: English
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