Encyclopedia of Complexity and Systems Science

Living Edition
| Editors: Robert A. Meyers

Embodied and Situated Agents, Adaptive Behavior in

Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27737-5_171-4

Definition of the Subject

Adaptive behavior concerns the study of how organisms develop their behavioral and cognitive skills through a synthetic methodology which consists in designing artificial agents which are able to adapt to their environment autonomously. These studies are important both from a modeling point of view (i.e., for making progress in our understanding of intelligence and adaptation in natural beings) and from an engineering point of view (i.e., for making progresses in our ability to develop artifacts displaying effective behavioral and cognitive skills).

Introduction

Adaptive behavior research concerns the study of how organisms can develop behavioral and cognitive skills by adapting to the environment and to the task they have to fulfill autonomously (i.e., without human intervention). This goal is achieved through a synthetic methodology, i.e., through the synthesis of artificial creatures which (i) have a body, (ii) are situated in an environment with which they...

Keywords

Cognitive Skill Complex Adaptive System Control Rule Behavioral Skill Light Gradient 
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.
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Bibliography

  1. Asada M, MacDorman K, Ishiguro H, Kuniyoshi Y (2001) Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robot Auton Syst 37:185–193MATHCrossRefGoogle Scholar
  2. Baldassarre G, Parisi D, Nolfi S (2006) Distributed coordination of simulated robots based on self-organisation. Artif Life 3(12):289–311CrossRefGoogle Scholar
  3. Beer RD (1995) A dynamical systems perspective on agent-environment interaction. Artif Intell 72:173–215CrossRefGoogle Scholar
  4. Beer RD (2003) The dynamics of active categorical perception in an evolved model agent. Adapt Behav 11:209–2CrossRefGoogle Scholar
  5. Berthouze L, Lungarella M (2004) Motor skill acquisition under environmental perturbations: on the necessity of alternate freezing and freeing. Adapt Behav 1(1):47–63CrossRefGoogle Scholar
  6. Bongard JC, Paul C (2001) Making evolution an offer it can’t refuse: morphology and the extradimensional bypass. In: Keleman J, Sosik P (eds) Proceedings of the sixth European conference on artificial life. Lecture notes in artificial intelligence, vol 2159. Springer, BerlinGoogle Scholar
  7. Breazeal C (2003) Towards sociable robots. Robot Auton Syst 42(3–4):167–175MATHCrossRefGoogle Scholar
  8. Brooks RA (1991a) Intelligence without reason. In: Mylopoulos J, Reiter R (eds) Proceedings of 12th international joint conference on artificial intelligence. Morgan Kaufmann, San MateoGoogle Scholar
  9. Brooks RA (1991b) Intelligence without reason. In: Proceedings of 12th international joint conference on artificial intelligence, Sydney, pp 569–595Google Scholar
  10. Brooks RA, Breazeal C, Irie R, Kemp C, Marjanovic M, Scassellati B, Williamson M (1998) Alternate essences of intelligence. In: Proceedings of the fifteenth national conference on artificial intelligence (AAAI-98), Madison, pp 961–976Google Scholar
  11. Chiel HJ, Beer RD (1997) The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Trends Neurosci 20:553–557CrossRefGoogle Scholar
  12. Clark A (1997) Being there: putting brain, body and world together again. MIT Press, CambridgeGoogle Scholar
  13. De Greef J, Nolfi S (2010) Evolution of implicit and explicit communication in a group of mobile robots. In: Nolfi S, Mirolli M (eds) Evolution of communication and language in embodied agents. Springer, BerlinGoogle Scholar
  14. Endo I, Yamasaki F, Maeno T, Kitano H (2002) A method for co-evolving morphology and walking patterns of biped humanoid robot. In: Proceedings of the IEEE conference on robotics and automation, Washington, DCGoogle Scholar
  15. Floreano D, Husband P, Nolfi S (2008) Evolutionary robotics. In: Siciliano B, Oussama K (eds) Handbook of robotics. Springer, BerlinGoogle Scholar
  16. Gigliotta O, Nolfi S (2008) On the coupling between agent internal and agent/environmental dynamics: development of spatial representations in evolving autonomous robots. Adapt Behav 16:148–165CrossRefGoogle Scholar
  17. Goldenberg E, Garcowski J, Beer RD (2004) May we have your attention: analysis of a selective attention task. In: Schaal S, Ijspeert A, Billard A, Vijayakumar S, Hallam J, Meyer J-A (eds) From animals to animats 8: proceedings of the eighth international conference on the simulation of adaptive behavior. MIT Press, CambridgeGoogle Scholar
  18. Harvey I (2000) Robotics: philosophy of mind using a screwdriver. In: Gomi T (ed) Evolutionary robotics: from intelligent robots to artificial life, vol III. AAI Books, OntarioGoogle Scholar
  19. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  20. Keijzer F (2001) Representation and behavior. MIT Press, LondonGoogle Scholar
  21. Kelso JAS (1995) Dynamics patterns: the self-organization of brain and behaviour. MIT Press, CambridgeGoogle Scholar
  22. Lungarella M, Metta G, Pfeifer R, Sandini G (2003) Developmental robotics: a survey. Connect Sci 15:151–190CrossRefGoogle Scholar
  23. Marocco D, Nolfi S (2007) Emergence of communication in embodied agents evolved for the ability to solve a collective navigation problem. Connect Sci 19(1):53–74CrossRefADSGoogle Scholar
  24. Massera G, Cangelosi A, Nolfi S (2007) Evolution of prehension ability in an anthropomorphic neurorobotic arm. Front Neurorobot 1(4):1–9Google Scholar
  25. McGeer T (1990) Passive walking with knees. In: Proceedings of the IEEE conference on robotics and automation, vol 2, pp 1640–1645Google Scholar
  26. Metta G, Sandini G, Natale L, Panerai F (2001) Development and Q30 robotics. In: Proceedings of IEEE-RAS international conference on humanoid robots, pp 33–42Google Scholar
  27. Mondada F, Franzi E, Ienne P (1993) Mobile robot miniaturisation: a tool for investigation in control algorithms. In: Proceedings of the third international symposium on experimental robotics. KyotoGoogle Scholar
  28. Mondada F, Pettinaro G, Guigrard A, Kwee I, Floreano D, Denebourg J-L, Nolfi S, Gambardella LM, Dorigo M (2004) Swarm-bot: a new distributed robotic concept. Auton Robot 17(2–3):193–221CrossRefGoogle Scholar
  29. Nolfi S (2002) Power and limits of reactive agents. Neurol Comput 49:119–145Google Scholar
  30. Nolfi S (2005) Behaviour as a complex adaptive system: on the role of self-organization in the development of individual and collective behaviour. Complexus 2(3–4):195–203Google Scholar
  31. Nolfi S, Floreano D (1999) Learning and evolution. Auton Robot 1:89–113CrossRefGoogle Scholar
  32. Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press/Bradford Books, CambridgeGoogle Scholar
  33. Nolfi S, Marocco D (2002) Active perception: a sensorimotor account of object categorization. In: Hallam B, Floreano D, Hallam J, Hayes G, Meyer J-A (eds) From animals to animats 7, Proceedings of the VII international conference on simulation of adaptive behavior. MIT Press, Cambridge, pp 266–271Google Scholar
  34. Oudeyer P-Y, Kaplan F, Hafner V (2007) Intrinsic motivation systems for autonomous mental development. IEEE Trans Evol Comput 11(2):265–286CrossRefGoogle Scholar
  35. Pfeifer R, Bongard J (2007) How the body shape the way we think. MIT Press, CambridgeGoogle Scholar
  36. Pfeifer R, Iida F, Gómez G (2006) Morphological computation for adaptive behavior and cognition. Int Congr Ser 1291:22–29CrossRefGoogle Scholar
  37. Pollack JB, Lipson H, Funes P, Hornby G (2001) Three generations of coevolutionary robotics. Artif Life 7:215–223CrossRefGoogle Scholar
  38. Prokopenko M, Gerasimov V, Tanev I (2006) Evolving spatiotemporal coordination in a modular robotic system. In: Rocha LM, Yaeger LS, Bedau MA, Floreano D, Goldstone RL, Vespignani A (eds) Artificial life X: proceedings of the tenth international conference on the simulation and synthesis of living systems. MIT Press, BostonGoogle Scholar
  39. Scassellati B (2001) Foundations for a theory of mind for a humanoid robot. PhD thesis, Department of Electrical Engineering and Computer Science, MIT, BostonGoogle Scholar
  40. Scheier C, Pfeifer R, Kunyioshi Y (1998) Embedded neural networks: exploiting constraints. Neural Netw 11:1551–1596CrossRefGoogle Scholar
  41. Schmidhuber J (2006) Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connect Sci 18(2):173–187CrossRefGoogle Scholar
  42. Schmitz A, Gómez G, Iida F, Pfeifer R (2007) On the robustness of simple speed control for a quadruped robot. In: Proceeding of the international conference on morphological computation, VeniceGoogle Scholar
  43. Slocum AC, Downey DC, Beer RD (2000) Further experiments in the evolution of minimally cognitive behavior: from perceiving affordances to selective attention. In: Meyer J, Berthoz A, Floreano D, Roitblat H, Wilson S (eds) From animals to animats 6. Proceedings of the sixth international conference on simulation of adaptive behavior. MIT Press, CambridgeGoogle Scholar
  44. Steels L (2003) Evolving grounded communication for robots. Trends Cogn Sci 7(7):308–312CrossRefGoogle Scholar
  45. Sugita Y, Tani J (2005) Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adapt Behav 13(1):33–52CrossRefGoogle Scholar
  46. Tani J, Fukumura N (1997) Self-organizing internal representation in learning of navigation: a physical experiment by the mobile robot Yamabico. Neural Netw 10(1):153–159CrossRefGoogle Scholar
  47. Tani J, Nolfi S (1999) Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Netw 12:1131–1141CrossRefGoogle Scholar
  48. Tani J, Nishimoto R, Namikawa J, Ito M (2008) Co-developmental learning between human and humanoid robot using a dynamic neural network model. IEEE Trans Syst Man Cybern B Cybern 38:1CrossRefGoogle Scholar
  49. Varela FJ, Thompson E, Rosch E (1991) The embodied mind: cognitive science and human experience. MIT Press, CambridgeGoogle Scholar
  50. van Gelder TJ (1998) The dynamical hypothesis in cognitive science. Behav Brain Sci 21:615–628Google Scholar
  51. Vaughan E, Di Paolo EA, Harvey I (2004) The evolution of control and adaptation in a 3D powered passive dynamic walker. In: Pollack J, Bedau M, Husband P, Ikegami T, Watson R (eds) Proceedings of the ninth international conference on the simulation and synthesis of living systems. MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute of Cognitive Sciences and TechnologiesNational Research Council (CNR)RomeItaly