Advertisement

Intrinsically Motivated Learning Systems: An Overview

  • Gianluca Baldassarre
  • Marco Mirolli
Chapter

Abstract

This chapter introduces the field of intrinsically motivated learning systems and illustrates the content, objectives, and organisation of the book. The chapter first expands the concept of intrinsic motivations, then introduces a taxonomy of three classes of intrinsic-motivation mechanisms (based on predictors, on novelty detection, and on competence), and finally introduces and reviews the various contributions of the book. The contributions are organised in six parts. The contributions of the first part provide general overviews on the concept of intrinsic motivations, the possible mechanisms that may implement them, and the functions that they can play. The contributions of the second, third, and fourth part focus on the three classes of the aforementioned intrinsic-motivation mechanisms. The contributions of the fifth part discuss mechanisms that are complementary to intrinsic motivations. The contributions of the sixth part introduce tools and experimental paradigms that can be used to investigate intrinsic motivations.

Keywords

Intrinsic Motivation Reinforcement Learning Humanoid Robot Extrinsic Motivation Learning Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This paper and a large part of the research reported in 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) and “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 “Acknowledgment” section of each chapter. The editors of this book thank the EU reviewers (Benjamin Kuipers, Luc Berthouze, and Yasuo Kuniyoshi) and the EU project officer (Cécile Huet) for their valuable advices and their encouragement. For more information on the IM-CLeVeR project and for additional multimedia material, see the project website: http://www.im-clever.eu/. We also thank Simona Bosco for her editorial help with some contributions.

References

  1. .
    Baldassarre, G.: What are intrinsic motivations? A biological perspective. In: Cangelosi, A., Triesch, J., Fasel, I., Rohlfing, K., Nori, F., Oudeyer, P.-Y., Schlesinger, M., Nagai, Y. (eds.) Proceedings of the International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob-2011), pp. E1–E8. Frankfurt Germany, 24–27 August, 2011Google Scholar
  2. .
    Baldassarre, G., Mirolli, M.: Deciding which skill to learn when: Temporal-difference competence-based intrinsic motivation (td-cb-im). In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  3. .
    Barto, A., Singh, S., Chentanez, N.: Intrinsically motivated learning of hierarchical collections of skills. In: International Conference on Developmental Learning (ICDL), LaJolla, CA, 20–22 October, 2004Google Scholar
  4. .
    Barto, A.G.: Intrinsic motivation and reinforcement learning. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  5. .
    Dayan, P.: Exploration from generalisation mediated by multiple controllers. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  6. .
    Deci, E.: Intrinsic Motivation. Plenum, New York (1975)CrossRefGoogle Scholar
  7. .
    Deci, E.L., Ryan, R.M.: Intrinsic motivation and self-determination in human behavior. Plenum, New York (1985)CrossRefGoogle Scholar
  8. .
    Gurney, K., Lepora, N., Shah, A., Koene, A., Redgrave, P.: Action discovery and intrinsic motivation: A biologically constrained formalisation. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  9. .
    Harlow, H.F.: Learning and satiation of response in intrinsically motivated complex puzzle performance by monkeys. J. Comp. Physiol. Psychol. 43, 289–294 (1950)CrossRefGoogle Scholar
  10. .
    Hart, S., Grupen, R.: Learning generalizable control programs. IEEE Trans. Auton. Mental Dev. 3(1) (2011)Google Scholar
  11. .
    Hart, S., Grupen, R.: Intrinsically motivated affordance discovery and modeling. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  12. .
    Harter, S.: A new self-report scale of intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Dev. Psychol. 17, 100–112 (1981)CrossRefGoogle Scholar
  13. .
    Houkes, I., Janssen, P., de Jong, J., Nijhuis, F.: Specific relationships between work characteristics and intrinsic work motivation, burnout and turnover intention: A multi-sample analysis. Eur. J. Work Org. Psychol. 10, 1–23 (2001)CrossRefGoogle Scholar
  14. .
    Kakade, S., Dayan, P.: Dopamine: Generalization and bonuses. Neural Netw. 15(4–6), 549–559 (2002)CrossRefGoogle Scholar
  15. .
    Kohn, A.: Punished by Rewards. Houghton Mifflin Boston, MA (1993)Google Scholar
  16. .
    Lisman, J.E., Grace, A.A.: The hippocampal-vta loop: Controlling the entry of information into long-term memory. Neuron 46(5), 703–713 (2005)CrossRefGoogle Scholar
  17. .
    Merrick, K., Maher, M.: Motivated Reinforcement Learning: Curious Characters for Multiuser Games. Springer, Berlin (2009)CrossRefGoogle Scholar
  18. .
    Merrick, K.E.: Novelty and beyond: Towards combined motivation models and integrated learning architectures. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  19. .
    Mirolli, M., Baldassarre, G.: Functions and mechanisms of intrinsic motivations: The knowledge versus competence distinction. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  20. .
    Natale, L., Nori, F., Metta, G., Fumagalli, M., Ivaldi, S., Pattacini, U., Randazzo, M., Schmitz, A., Sandini, G.: The icub platform: A tool for studying intrinsically motivated learning. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  21. .
    Nehmzow, U., Gatsoulis, Y., Kerr, E., Condell, J., Siddique, N.H., McGinnity, M.T.: Novelty detection as an intrinsic motivation for cumulative learning robots. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  22. .
    Neto, H.V., Nehmzow, U.: Visual novelty detection with automatic scale selection. Robot. Auton. Syst. 55(9), 693–701 (2007)CrossRefGoogle Scholar
  23. .
    Ornkloo, H., Hofsten, C.v.: Fitting objects into holes: On the development of spatial cognition skills. Dev. Psychol. 43(2), 404–416 (2006)Google Scholar
  24. .
    Otmakova, N., Duzel, E., Deutch, A.Y., Lisman, J.E.: The hippocampal-VTA loop: The role of novelty and motivation in controlling the entry of information into long-term memory. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  25. .
    Oudeyer, P.-Y., Banares, A., Frédéric, K.: Intrinsically motivated learning of real world sensorimotor skills with developmental constraints. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  26. .
    Oudeyer, P.-Y., Kaplan, F.: What is intrinsic motivation? a typology of computational approaches. Front. Neurorobot. 1, 6 (2007)CrossRefGoogle Scholar
  27. .
    Oudeyer, P.-Y., Kaplan, F., Hafner, V.V.: Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11(2), 265–286 (2007)CrossRefGoogle Scholar
  28. .
    Redgrave, P., Gurney, K.: The short-latency dopamine signal: A role in discovering novel actions? Nat. Rev. Neurosci. 7(12), 967–975 (2006)CrossRefGoogle Scholar
  29. .
    Redgrave, P., Gurney, K., Stafford, T., Thirkettle, M., Lewis, J.: The role of the basal ganglia in discovering novel actions. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  30. .
    Redgrave, P., Vautrelle, N., Reynolds, J.N.J.: Functional properties of the basal ganglia’s re-entrant loop architecture: Selection and reinforcement. Neuroscience vol. 198 pp. 138–151 (2011)CrossRefGoogle Scholar
  31. .
    Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55(1), 68–78 (2000)CrossRefGoogle Scholar
  32. .
    Schembri, M., Mirolli, M., Baldassarre, G.: Evolution and learning in an intrinsically motivated reinforcement learning robot. In: Almeida e Costa Fernando, Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) Advances in Artificial Life. Proceedings of the 9th European Conference on Artificial Life (ECAL2007), Lisbon, Portugal, 10–14 September 2007. Lecture Notes in Artificial Intelligence, vol. 4648, pp. 294–333. Springer, Berlin (2007a)Google Scholar
  33. .
    Schembri, M., Mirolli, M., Baldassarre, G.: Evolving childhood’s length and learning parameters in an intrinsically motivated reinforcement learning robot. In: Berthouze, L., Dhristiopher, P.G., Littman, M., Kozima, H., Balkenius, C. (eds.) Proceedings of the Seventh International Conference on Epigenetic Robotics, vol. 134, pp. 141–148 Lund, Sweden. Lund University Cognitive Studies vol. 149 (2007b)Google Scholar
  34. .
    Schembri, M., Mirolli, M., Baldassarre, G.: Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot. In: Demiris, Y., Mareschal, D., Scassellati, B., Weng, J. (eds.) Proceedings of the 6th International Conference on Development and Learning, pp. E1–E6. Imperial College, London, UK, 11–13 July (2007c)Google Scholar
  35. .
    Schlesinger, M.: In:vestigating the origins of intrinsic motivations in human infants. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  36. .
    Schmidhuber, J.: Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1458–1463. Singapore 18–21 November (1991a)Google Scholar
  37. .
    Schmidhuber, J.: A possibility for implementing curiosity and boredom in model-building neural controllers. In: Meyer, J.-A., Wilson, S. (eds.) From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pp. 222–227. Paris, France, December, 1990. The MIT Press, Cambridge (1991b)Google Scholar
  38. .
    Schmidhuber, J.: Formal theory of creativity, fun, and intrinsic motivation (1990–2010): IEEE Trans. Auton. Mental Dev. 2(3), 230–247 (2010)CrossRefGoogle Scholar
  39. .
    Schmidhuber, J.: Maximizing fun by creating data with easily reducible subjective complexity. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  40. .
    Singh, S., Barto, A., Chentanez, N.: Intrinsically motivated reinforcement learning. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17: Proceedings of the 2004 Conference, Vancouver, British Columbia, Canada, 13–18 December 2004. MIT, Cambridge (2005)Google Scholar
  41. .
    Singh, S., Lewis, R., Barto, A., Sorg, J.: Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Trans. Auton. Mental Dev. 2(2), 70–82 (2010)CrossRefGoogle Scholar
  42. .
    Stafford, T., Walton, T., Hetherington, L., Thirkettle, M., Gurney, K., Redgrave, P.: A novel behavioural task for researching intrinsic motivation. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  43. .
    Taffoni, F., Formica, D., Schiavone, G., Scorcia, M., Tomassetti, A., Polizzi di Sorrentino, E., Sabbatini, G., Truppa, V., Mirolli, M., Baldassarre, G., Visalberghi, E., Keller, F, Guglielmelli, E.: The “mechatronic board”: A tool to study intrinsic motivation in humans, animals and robots. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin (2012, this volume)Google Scholar
  44. .
    Vieiraneto, H., Nehmzow, U.: Visual novelty detection with automatic scale selection. Robot. Auton. Syst. 55, 693–701 (2007)CrossRefGoogle Scholar
  45. .
    von Hofsten, C.: Action in development. Dev. Sci. 10(1), 54–60 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Cognitive Sciences and Technologies, CNRRomaItaly

Personalised recommendations