Computational and Robotic Models of the Hierarchical Organization of Behavior

  • Gianluca Baldassarre
  • Marco Mirolli

Table of contents

  1. Front Matter
    Pages i-vi
  2. Hierarchical Organization of Behavior in Robots

    1. Front Matter
      Pages 11-11
    2. Andrew G. Barto, George Konidaris, Christopher Vigorito
      Pages 13-46
    3. Jonathan Mugan, Benjamin Kuipers
      Pages 63-80
    4. Dimitri Ognibene, Yan Wu, Kyuhwa Lee, Yiannis Demiris
      Pages 81-98
  3. Hierarchical Organization of Animal Behavior

    1. Front Matter
      Pages 127-127
    2. Stephan Ehrenfeld, Oliver Herbort, Martin V. Butz
      Pages 129-154
    3. Luca Lonini, Christos Dimitrakakis, Constantin Rothkopf, Jochen Triesch
      Pages 155-176
    4. Mark Lee, James Law, Martin Hülse
      Pages 177-212
    5. Encarni Marcos, Milanka Ringwald, Armin Duff, Martí Sánchez-Fibla, Paul F. M. J. Verschure
      Pages 213-234
  4. Hierarchical Organization of Animal Brain

    1. Front Matter
      Pages 235-235
    2. Carlos Diuk, Anna Schapiro, Natalia Córdova, José Ribas-Fernandes, Yael Niv, Matthew Botvinick
      Pages 271-291
    3. Juan M. Galeazzi, Simon M. Stringer
      Pages 293-317
    4. Henry H. Yin
      Pages 319-347
  5. Back Matter
    Pages 349-358

About this book


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. 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.


AI Alife artificial intelligence artificial life brain science cognition control embodiment evolution learning neural networks neuroscience perception reinforcement learning robotics

Editors and affiliations

  • Gianluca Baldassarre
    • 1
  • Marco Mirolli
    • 2
  1. 1.Consiglio Nazionale delle RicercheIstituto di Scienze e Tecnologie della CognizioneRomeItaly
  2. 2.Consiglio Nazionale delle RicercheIstituto di Scienze e Tecnologie della CognizioneRomeItaly

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-39874-2
  • Online ISBN 978-3-642-39875-9
  • Buy this book on publisher's site