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Behavioural plasticity in evolving robots

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Abstract

In this paper, we show how the development of plastic behaviours, i.e., behaviour displaying a modular organisation characterised by behavioural subunits that are alternated in a context-dependent manner, can enable evolving robots to solve their adaptive task more efficiently also when it does not require the accomplishment of multiple conflicting functions. The comparison of the results obtained in different experimental conditions indicates that the most important prerequisites for the evolution of behavioural plasticity are: the possibility to generate and perceive affordances (i.e., opportunities for behaviour execution), the possibility to rely on flexible regulatory processes that exploit both external and internal cues, and the possibility to realise smooth and effective transitions between behaviours.

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References

  • Ackerman E (2010) Long exposure pictures of robots cleaning [Blog post]. http://spectrum.ieee.org/automaton/robotics/home-robots/long-exposure-pictures-of-robots-cleaning. Retrieved April 23, 2015

  • Bangard J (2011) Spontaneous evolution of structural modularity in robot neural network controller (2011). In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation (GECCO), ACM, New York

  • Barlow GW (1977) Modal action patterns. In: Sebeok TA (ed) How animals communicate. Indiana University Press, Bloomington

    Google Scholar 

  • Bonani M, Longchamp V, Magnenat S, Retornaz P, Burnier D, Roulet G, Vaussard F, Bleuler H, Mondada F (2010) The marXbot, a miniature mobile robot opening new perspectives for the collective robotic research. In: 2010 IEEE/RSJ international conference on intelligent robots and systems, pp 4187–4193

  • Brooks RA (1986) A robust layered control system for a mobile robot. Robot Autom 2(1):14–22

    Google Scholar 

  • Calabretta R, Nolfi S, Parisi D, Wagner G (2000) Duplication of modules facilitates the evolution of functionalspecialization. Artif Life 6(1):69–84

    Article  CAS  PubMed  Google Scholar 

  • Chemero A (2011) Radical embodied cognitive science. MIT Press, Cambridge

    Google Scholar 

  • Conrad M (1990) The geometry of evolution. BioSystems 24:61–81

    Article  CAS  PubMed  Google Scholar 

  • Der R, Martius G (2012) The playful machine: theoretical foundation and practical realization of self-organizing robots. Springer, Berlin

    Book  Google Scholar 

  • Eberhard WG (1988) Behavioural flexibility in orb web construction: effect of supplies in different silk glands and skider size and weight. J Arachnol 16:295–302

    Google Scholar 

  • Fentress JC (1983) A view of ontogeny. In: Esenberg J, Kleiman D (eds) Special publications American Society of Mammalogists, vol 7, pp 24–64

  • Floreano D, Nolfi S (1997) Adaptive behavior in competing co-evolving species. In: Husband P, Harvey I (eds) Proceedings of the fourth conference on artificial life. MIT Press, Cambridge

    Google Scholar 

  • Gallistel CR (1980) The organization of action. A new synthesis. Lawrence Erlbaum, Hillsdale

    Google Scholar 

  • Gibson J (1979) The ecological approach to visual perception. Houghton-Mifflin, Boston

    Google Scholar 

  • Gordon G, Fonio E, Ahissar E (2014) Emergent exploration via novelty management. J Neurosci 34(38):12646–12661

    Article  CAS  PubMed  Google Scholar 

  • Haruno M, Wolpert DM, Kawato M (2001) Mosaic model for sensorimotor learning and control. Neural Comput 13:2201–2220

    Article  CAS  PubMed  Google Scholar 

  • Hinde RA (1970) Animal behavior. McGraw Hill, New York

    Google Scholar 

  • Huizinga J, Mouret B, Clune J (2014) Evolving neural networks that are both modular and regular: hyperneatplus the connection cost technique. In: Proceedings of the genetic and evolutionary computation conference (GECCO), ACM, New York

  • IRobot (2013) Our history. http://www.irobot.com/about-irobot/company-information/history.aspx. Retrieved April 29, 2015

  • Izquierdo E, Bührmann T (2008) Analysis of a dynamical recurrent neural network evolved for two qualitatively different tasks: walking and chemotaxis. In: Bullock S, Noble J, Watson RA, Bedau MA (eds) Proceedings of the 11th international conference on the synthesis and simulation of living systems (ALIFE 11). MIT Press, Cambridge

    Google Scholar 

  • Jackson RR, Wilcox RS (1993) Spider flexibly chooses aggressive mimicry signals for different prey by trial and errors. Behavior 127:21–36

    Article  Google Scholar 

  • Komers PE (1997) Behavioural plasticity in variable environments. Can J Zool 75:161–169

    Article  Google Scholar 

  • Krischner MW, Gerhart JC (2005) The plausibility of life: resolving Darwin’s Dilemma. Yale University Press, USA

    Google Scholar 

  • Martius G, Der R, Herrmann JM (2014) Robot learning by guided self-organization. Springer, Berlin

    Book  Google Scholar 

  • Massera G, Ferrauto T, Gigliotta O, Nolfi S (2013) FARSA: an open software tool for embodied cognitive science. In: Lio’ P, Miglino O, Nicosia G, Nolfi S, Pavone M (eds) Proceeding of the 12th European conference on artificial life. MIT Press, Cambridge

    Google Scholar 

  • Mitchell SD (1990) The units of behaviour in evolutionary explanations. In: Bekoff M, Jamieson D (eds) Interpretation and Explanation in the Study of Animal Behavior. Westview Press, Boulder

    Google Scholar 

  • Montes Gonzalez F, Prescott TJ, Gurney K Humphries M, Redgrave P (2000) An embodied model of action selection mechanisms in the vertebrate brain. In: Meyer J-A, Berthoz A, Floreano D, Roitblat H, Wilson SW (eds) From animals to animats 6: Proceedings of the sixth international conference on simulation of adaptive behaviour. MIT Press, Cambridge

    Google Scholar 

  • Nolfi S (2009) Behavior and cognition as a complex adaptive system: insights from robotic experiments. In: Hooker C (ed) Handbook of the philosophy of science: philosophy of complex systems, vol 10. General editors: Dov M. Gabbay, Paul Thagard, John Woods. Elsevier

  • Nolfi S, Floreano D (1999) Learning and evolution. Auton Robot 7:89–113

    Article  Google Scholar 

  • Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press/Bradford Books, Cambridge

    Google Scholar 

  • Nolfi S, Parisi D (1997) Learning to adapt to changing environments in evolving neural networks. Adapt Behav 1:75–98

    Google Scholar 

  • Nolfi S, Bongard J, Husband P, Floreano D (2016) Evolutionary robotics. In: Siciliano B, Khatib O (eds) Handbook of robotics, Springer, Berlin (in press)

  • Otte T (1972) Simple versus elaborate behavior in grasshoppers. An analysis of communication in the genus Syrbula. Behaviour 42:291–322

    Article  Google Scholar 

  • Oudeyer P-Y, Kaplan F, Hafner V (2007) Intrinsic motivation systems for autonomous mental development. IEEE Trans Evol Comput 11(2):265–286

    Article  Google Scholar 

  • Petrosino G, Parisi D, Nolfi S (2013) Selective attention enables action selection: evidence from evolutionary robotics experiments. Adapt Behav 21(5):356–370

    Article  Google Scholar 

  • Prescott TJ (2008) Action selection. Scholarpedia 3(2):2705

    Article  Google Scholar 

  • Rahim SA, Yusof AM, Braunl T (2014) Genetically evolving action selection mechanisms in a behavior-based system for target tracking. Neurocomputing 133:84–94

    Article  Google Scholar 

  • Schmidhuber J (1990) A possibility for implementing curiosity and boredom in model-building neural controllers. In: From animals to animals: proceedings of the first international conference on simulation of adaptive behavior. MIT Press, Cambridge

  • Schrumand J, Miikkulainen R (2012) Evolving multimodal networks for multitask games. IEEE Trans Comput Intell AI Games 4(2):94–111

    Article  Google Scholar 

  • Seth A (2012) Optimized agent based modelling of action selection. In: Seth A, Prescott TJ, Brysonn JJ (eds) Modelling natural action selection. Cambridge University Press, Cambridge

    Google Scholar 

  • Seth A, Prescott TJ, Brysonn JJ (2012) Modelling natural action selection. Cambridge University Press, Cambridge

    Google Scholar 

  • Stone P, Veloso M (2000) Layered learning. In ECML. Springer, Berlin, pp 369–381

  • Tani J, Ito M (2007) Self-organization of behavioural primitives as multiple attractor dynamics: a robot experiment. IEEE Trans Syst Man Cybern. Part A Syst Hum 33(4):481–488

    Article  Google Scholar 

  • Tani J, Nolfi S (1999) Learning to perceive the world as articulated: an approach for hierarchical learning in sensory- motor systems. Neural Netw 12:1131–1141

    Article  PubMed  Google Scholar 

  • Van Hoorn N, Togelius J, Schmidhuber J (2009) Hierarchical controller learning in a first-person shooter. In CIG. IEEE, pp 294–301

  • Verbancsics P, Stanley KO (2011) Constraining connectivity to encourage modularity in HyperNEAT. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation (GECCO), ACM, New York

  • Wenzel JW (1993) Application of the biogenetic law to behavioural ontogeny: a test using nest architecture in paper wasps. J Evol Biol 6:229–247

    Article  Google Scholar 

  • Williams GC (1966) Adaptation and natural selection. Princeton University Press, Princeton

    Google Scholar 

  • West-Eberhard MJ (2003) Developmental plasticity and evolution. Oxford University Press, New York

    Google Scholar 

  • Williams P, Beer R (2013) Environmental feedback drives multiple behaviors from the same neural circuit. In Advances in artificial life, ECAL 2013, vol 12, pp 268–275

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Acknowledgments

This work is partially funded by CAPES through the Brazilian program Science Without Borders. The authors thank Vito Trianni, Luca Simione, and the anonymous reviewers for useful comments on the first drafts of the paper.

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Authors and Affiliations

  1. Institute of Cognitive Sciences and Technologies, National Research Council (CNR), Via S. Martino della Battaglia, 44, 00185, Rome, Italy

    Jônata Tyska Carvalho & Stefano Nolfi

  2. Center for Computational Sciences (C3), Federal University of Rio Grande (FURG), Av. Italia, km 8, Rio Grande, 96203-900, Brazil

    Jônata Tyska Carvalho

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  1. Jônata Tyska Carvalho
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  2. Stefano Nolfi
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Correspondence to Jônata Tyska Carvalho.

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Carvalho, J.T., Nolfi, S. Behavioural plasticity in evolving robots. Theory Biosci. 135, 201–216 (2016). https://doi.org/10.1007/s12064-016-0233-y

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