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Novelty of behaviour as a basis for the neuro-evolution of operant reward learning

Published: 08 July 2009 Publication History

Abstract

An agent that deviates from a usual or previous course of action can be said to display novel or varying behaviour. Novelty of behaviour can be seen as the result of real or apparent randomness in decision making, which prevents an agent from repeating exactly past choices. In this paper, novelty of behaviour is considered as an evolutionary precursor of the exploring skill in reward learning, and conservative behaviour as the precursor of exploitation. Novelty of behaviour in neural control is hypothesised to be an important factor in the neuro-evolution of operant reward learning. Agents capable of varying behaviour, as opposed to conservative, when exposed to reward stimuli appear to acquire on a faster evolutionary scale the meaning and use of such reward information. The hypothesis is validated by comparing the performance during evolution in two environments that either favour or are neutral to novelty. Following these findings, we suggest that neuro-evolution of operant reward learning is fostered by environments where behavioural novelty is intrinsically beneficial, i.e. where varying or exploring behaviour is associated with low risk.

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  • (2016)Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their LifetimePLOS ONE10.1371/journal.pone.016223511:9(e0162235)Online publication date: 2-Sep-2016
  • (2016)Accelerating the Evolution of Cognitive Behaviors Through Human-Computer CollaborationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908928(133-140)Online publication date: 20-Jul-2016
  • (2016)Behavioral plasticity through the modulation of switch neuronsNeural Networks10.1016/j.neunet.2015.11.00174:C(35-51)Online publication date: 1-Feb-2016
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    cover image ACM Conferences
    GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
    July 2009
    2036 pages
    ISBN:9781605583259
    DOI:10.1145/1569901
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 08 July 2009

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    Author Tags

    1. artificial life
    2. learning
    3. neuro-evolution

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    GECCO09
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    GECCO09: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2009
    Québec, Montreal, Canada

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    Cited By

    View all
    • (2016)Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their LifetimePLOS ONE10.1371/journal.pone.016223511:9(e0162235)Online publication date: 2-Sep-2016
    • (2016)Accelerating the Evolution of Cognitive Behaviors Through Human-Computer CollaborationProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908928(133-140)Online publication date: 20-Jul-2016
    • (2016)Behavioral plasticity through the modulation of switch neuronsNeural Networks10.1016/j.neunet.2015.11.00174:C(35-51)Online publication date: 1-Feb-2016
    • (2015)Searching for novelty in pole balancing2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257104(1792-1798)Online publication date: May-2015
    • (2014)Overcoming deception in evolution of cognitive behaviorsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598300(185-192)Online publication date: 12-Jul-2014
    • (2014)Artificial Evolution of Plastic Neural Networks: A Few Key ConceptsGrowing Adaptive Machines10.1007/978-3-642-55337-0_9(251-261)Online publication date: 5-Jun-2014
    • (2013)On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural NetworksPLoS ONE10.1371/journal.pone.00791388:11(e79138)Online publication date: 13-Nov-2013
    • (2011)On the relationships between synaptic plasticity and generative systemsProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001782(1531-1538)Online publication date: 12-Jul-2011
    • (2011)Using a map-based encoding to evolve plastic neural networks2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS.2011.5945909(9-16)Online publication date: Apr-2011
    • (2011)Improving evolvability through novelty search and self-adaptation2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949955(2693-2700)Online publication date: Jun-2011
    • Show More Cited By

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