| Description |
1 online resource (xvi, 288 pages) : illustrations |
| Series |
Lecture notes in artificial intelligence, subseries of Lecture notes in computer science, 0302-9743 ; 5225 |
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State-of-the-art survey |
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LNCS sublibrary. SL 7, Artificial intelligence |
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Lecture notes in computer science ; 5225
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Lecture notes in computer science. Lecture notes in artificial intelligence
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Lecture notes in computer science. State-of-the-art survey
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LNCS sublibrary. SL 7, Artificial intelligence
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| Contents |
Introduction: Anticipation in natural and artificial cognition / Giovanni Pezzulo [and others] -- The anticipatory approach: definitions and taxonomies / Giovanni Pezzulo, Martin V. Butz and Cristiano Castelfranchi -- Benefits of anticipations in cognitive agents / Martin V. Butz and Giovanni Pezzulo -- Anticipation in attention / Christian Balkenius [and others] -- Anticipatory, goal-directed behavior / Martin V. Butz, Oliver Herbort and Giovanni Pezzulo -- Anticipation and believability / Carlos Martinho and Ana Paiva -- Anticipation and emotions for goal directed agents / Emiliano Lorini [and others] -- A reinforcement-learning model of top-down attention based on a potential-action map / Dimitri Ognibene, Christian Balkenius and Gianluca Baldassarre -- Anticipation by analogy / Boicho Kokinov [and others] -- Anticipation in coordination / Maurice Grinberg and Emilian Lalev -- Endowing artificial systems with anticipatory capabilities: success cases / Giovanni Pezzulo [and others] |
| Summary |
This book proposes a unifying approach for the analysis and design of artificial cognitive systems: The Anticipatory Approach. In 11 coherent chapters, the authors of this State-of-the-Art Survey propose a foundational view of the importance of dealing with the future, of gaining some autonomy from current environmental data, and of endogenously generating sensorimotor and abstract representations. A meaningful taxonomy for anticipatory cognitive mechanisms is put forward, which distinguishes between the types of predictions and the different influences of these predictions on actual behavior and learning. Thus a new unifying perspective on cognitive systems is given. The Anticipatory Approach described in this book will not only aid in the analysis of cognitive systems, but will also serve as an inspiration and guideline for the progressively more advanced and competent design of large, but modular, artificial cognitive systems |
| Bibliography |
Includes bibliographical references (pages 255-288) |
| Notes |
Print version record |
| Subject |
Expectation (Psychology) -- Mathematical models
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Cognitive learning theory -- Mathematical models
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Cognitive neuroscience -- Mathematical models
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Brain -- Mathematical models.
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Informatique.
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Brain -- Mathematical models.
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Expectation (Psychology) -- Mathematical models.
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| Form |
Electronic book
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| Author |
Pezzulo, Giovanni
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| ISBN |
9783540877028 |
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3540877029 |
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