[go: up one dir, main page]

Skip to main content

Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview

  • Chapter
  • First Online:
Computational and Robotic Models of the Hierarchical Organization of Behavior

Abstract

The hierarchical organisation of behaviour is a fundamental means through which robots and organisms can acquire and produce sophisticated and flexible behaviours that allow them to solve multiple tasks in multiple conditions. Recently, the research on this topic has been receiving increasing attention. On the one hand, machine learning and robotics are recognising the fundamental importance of the hierarchical organisation of behaviour for building robots that scale up to solve complex tasks, possibly in a cumulative fashion. On the other hand, research in psychology and neuroscience is finding increasing evidence that modularity and hierarchy are pivotal organisation principles of behaviour and of the brain. This book reviews the state of the art in computational and robotic models of the hierarchical organisation of behaviour. Each contribution reviews the main works of the authors on this subject, the open challenges, and promising research directions. Together, the contributions give a good coverage of the most important models, findings, and challenges of the field. This introductory chapter presents the general aims and scope of the book and briefly summarises the contents of each chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 15.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Bakker, B., & Schmidhuber, J. (2004). Hierarchical reinforcement learning based on subgoal discovery and subpolicy specialization. In F. Groen, N. Amato, A. Bonarini, E. Yoshida, B. Kruse (Eds.), Proceedings of the 8-th conference on intelligent autonomous systems (IAS-8) (pp. 438–445).

    Google Scholar 

  • Baldassarre, G., & Mirolli, M. (2010). What are the key open challenges for understanding the autonomous cumulative learning of skills? The Newsletters of the Autonomous Mental Development Technical committee (IEEE CIS AMD Newsletters), 7(1), 11.

    Google Scholar 

  • Balleine, B. W., & Dickinson, A. (1998). Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology, 37(4–5), 407–419.

    Article  Google Scholar 

  • Botvinick, M., & Plaut, D.C. (2004). Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111(2), 395–429.

    Article  Google Scholar 

  • Cooper, R., & Shallice, T. (2000). Contention scheduling and the control of routine activities. Cognitive Neuropsychology, 17(4), 297–338.

    Article  Google Scholar 

  • Demiris, Y., & Khadhouri, B. (2006). Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems, 54(5), 361–369.

    Article  Google Scholar 

  • Fischer, K. W. (1980). A theory of cognitive development: the control and construction of hierarchies of skills. Psychological Review, 87(6), 477–531.

    Article  Google Scholar 

  • French, R. M. (1999). Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences, 3(4), 128–135.

    Article  MathSciNet  Google Scholar 

  • Fuster, J. M. (2001). The prefrontal cortex–an update: time is of the essence. Neuron, 30, 319–333.

    Article  Google Scholar 

  • Graybiel, A. M. (1998). The basal ganglia and chunking of action repertoires. Neurobiology of Learning and Memory, 70(1–2), 119–136.

    Article  Google Scholar 

  • Graziano, M. (2006). The organization of behavioral repertoire in motor cortex. The Annual Review of Neuroscience, 29, 105–134.

    Article  Google Scholar 

  • Hart, S., & Grupen, R. (2011). Learning generalizable control programs. IEEE Transactions on Autonomous Mental Development, 3(1), 216–231.

    Article  Google Scholar 

  • McCloskey, M., & Cohen, N. (1989). Catastrophic interference in connectionist networks: the sequential learning problem. In G. H. Bower (Ed.), The psychology of learning and motivation (vol. 24, pp. 109–165). San Diego: Academic.

    Google Scholar 

  • Meunier, D., Lambiotte, R., Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Front Neuroscience, 4, 200.

    Google Scholar 

  • Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. The Annual Review of Neuroscience, 24, 167–202.

    Article  Google Scholar 

  • Miller, G. A., Galanter, E., Pribram, K. H. (1960). Plans and the structure of behavior. New York: Adams-Bannister-Cox.

    Book  Google Scholar 

  • Redgrave, P., & Gurney, K. (2006). The short-latency dopamine signal: a role in discovering novel actions? Nature Reviews Neuroscience, 7(12), 967–975.

    Article  Google Scholar 

  • Schneider, D. W., & Logan, G. D. (2006). Hierarchical control of cognitive processes: switching tasks in sequences. Journal of Experimental Psychology. General, 135(4), 623–640.

    Article  Google Scholar 

  • Singh, S. (1992). Transfer of learning by composing solutions of elemental sequential tasks. Machine Learning, 8(3), 323–339.

    MATH  Google Scholar 

  • Yamashita, Y., & Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Computational Biology, 4(11), e1000220.

    Article  Google Scholar 

  • Yin, H. H., & Knowlton, B. J. (2006). The role of the basal ganglia in habit formation. Nature Reviews Neuroscience, 7(6), 464–476.

    Article  Google Scholar 

  • Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., Reynolds, J. R. (2007). Event perception: a mind-brain perspective. Psychological Bulletin, 133(2), 273–293.

    Article  Google Scholar 

Download references

Acknowledgements

This chapter and a large part of the effort that led to this book have been supported by the Project “IM-CLeVeR—Intrinsically Motivated Cumulative Learning Versatile Robots” funded by the European Commission under the 7th Framework Programme (FP7/2007–2013), “Challenge 2—Cognitive Systems, Interaction, Robotics”, Grant Agreement No. ICT-IP-231722. Support or co-support from other institutions, where present, is described in the “Acknowledgments” section of each chapter. The editors of the book thank the EU reviewers (Benjamin Kuipers, Luc Berthouze, and Yasuo Kuniyoshi) and the EU Project Officer (Cécile Huet) for their valuable advice and encouragement. For more information on the IM-CLeVeR project, and for additional multimedia material, see the project web site: http://www.im-clever.eu/. We also thank Simona Bosco for her editorial help with some contributions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gianluca Baldassarre .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Baldassarre, G., Mirolli, M. (2013). Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview. In: Baldassarre, G., Mirolli, M. (eds) Computational and Robotic Models of the Hierarchical Organization of Behavior. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39875-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39875-9_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39874-2

  • Online ISBN: 978-3-642-39875-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics