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008			131113s2013 gw | s |||| 0|eng d
020			_a9783642398759 _9978-3-642-39875-9
024	7		_a10.1007/978-3-642-39875-9 _2doi
050		4	_aQ334-342
050		4	_aTJ210.2-211.495
072		7	_aUYQ _2bicssc
072		7	_aTJFM1 _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
245	1	0	_aComputational and Robotic Models of the Hierarchical Organization of Behavior _h[electronic resource] / _cedited by Gianluca Baldassarre, Marco Mirolli.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013.
300			_aVI, 358 p. 116 illus., 73 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChap. 1 - Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview -- Chap. 2 - Behavioral Hierarchy: Exploration and Representation -- Chap. 3 - Self-organized Functional Hierarchy Through Multiple Timescales: Neurodynamical Accounts for Behavioral Compositionality -- Chap. 4 - Autonomous Representation Learning in a Developing Agent -- Chap. 5 - Hierarchies for Embodied Action Perception -- Chap. 6 - Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module Activation -- Chap. 7 - Modular, Multimodal Arm Control Models -- Chap. 8 - Generalization and Interference in Human Motor Control -- Chap. 9 - A Developmental Framework for Cumulative Learning Robots -- Chap. 10 - The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture -- Chap. 11 - The Hierarchical Organization of Cortical and Basal Ganglia Systems: A Computationally Informed Review and Integrated Hypothesis -- Chap. 12 - Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans -- Chap. 13 - Neural Network Modelling of Hierarchical Motor Function in the Brain -- Chap. 14 - Restoring Purpose in Behavior.
520			_aCurrent 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. The book chapters review the authors' most recent contributions to the investigation of hierarchical behavior, and highlight the open questions and most promising research directions. As the contributing authors are among the pioneers carrying out fundamental work on this topic, the book covers the most important and topical issues in the field from a computationally informed, theoretically oriented perspective. The book will be of benefit to academic and industrial researchers and graduate students in related disciplines.
650		0	_aComputer science.
650		0	_aNeurosciences.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650		0	_aControl engineering.
650		0	_aRobotics.
650		0	_aMechatronics.
650		0	_aExperiential research.
650	1	4	_aComputer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aControl, Robotics, Mechatronics.
650	2	4	_aComputational Intelligence.
650	2	4	_aNeurosciences.
650	2	4	_aPsychology Research.
700	1		_aBaldassarre, Gianluca. _eeditor.
700	1		_aMirolli, Marco. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642398742
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-39875-9
912			_aZDB-2-SCS
942			_2Dewey Decimal Classification _ceBooks
999			_c47468 _d47468