| Description |
1 online resource (365 pages) : illustrations |
| Contents |
Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview -- Gianluca Baldassarre and Marco Mirolli -- Hierarchical Organization of Behavior in Robots. Behavioral Hierarchy: Exploration and Representation / Andrew G. Barto, George Konidaris and Christopher Vigorito -- Self-Organized Functional Hierarchy Through Multiple Timescales: Neuro-dynamical Accounts for Behavioral Compositionality / Yuichi Yamashita and Jun Tani -- Autonomous Representation Learning in a Developing Agent / Jonathan Mugan and Benjamin Kuipers -- Hierarchies for Embodied Action Perception / Dimitri Ognibene [and others] -- Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module Activation / Constantin A. Rothkopf and Dana H. Ballard -- Hierarchical Organization of Animal Behavior. Modular, Multimodal Arm Control Models / Stephan Ehrenfeld, Oliver Herbort and Martin V. Butz -- Generalization and Interference in Human Motor Control / Luca Lonini [and others] -- A Developmental Framework for Cumulative Learning Robots / Mark Lee, James Law and Martin Hülse -- The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture / Encarni Marcos [and others] -- Hierarchical Organization of Animal Brain. The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis / Gianluca Baldassarre, Daniele Caligiore and Francesco Mannella -- Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans / Carlos Diuk [and others] -- Neural Network Modelling of Hierarchical Motor Function in the Brain / Juan M. Galeazzi and Simon M. Stringer -- Restoring Purpose in Behavior / Henry H. Yin |
| Summary |
Current robots and other artificial systems are typically able to accomplish only one single task. Overcoming this limitation requires the development of control architectures and learning algorithms that can support the acquisition and deployment of several different skills, which in turn seems to require a modular and hierarchical organization. In this way, different modules can acquire different skills without catastrophic interference, and higher-level components of the system can solve complex tasks by exploiting the skills encapsulated in the lower-level modules. While machine learning and robotics recognize the fundamental importance of the hierarchical organization of behavior for building robots that scale up to solve complex tasks, research in psychology and neuroscience shows increasing evidence that modularity and hierarchy are pivotal organization principles of behavior and of the brain. They might even lead to the cumulative acquisition of an ever-increasing number of skills, which seems to be a characteristic of mammals, and humans in particular. This book is a comprehensive overview of the state of the art on the modeling of the hierarchical organization of behavior in animals, and on its exploitation in robot controllers. The book perspective is highly interdisciplinary, featuring models belonging to all relevant areas, including machine learning, robotics, neural networks, and computational modeling in psychology and neuroscience |
| Notes |
Includes index |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| In |
Springer eBooks |
| Subject |
Computational learning theory.
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Robotics.
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Mathematical models.
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Human behavior.
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Robotics
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Models, Theoretical
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Behavior
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mathematical models.
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human behavior.
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COMPUTERS -- General.
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Robotics
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Mathematical models
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Human behavior
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Computational learning theory
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| Form |
Electronic book
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| Author |
Baldassarre, Gianluca, editor
|
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Mirolli, Marco, editor
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| ISBN |
9783642398759 |
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3642398758 |
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364239874X |
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9783642398742 |
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