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2010. 05. 11 Jongwon Yoon

Evolutionary conditions for the emergence of communication in robots Dario Floreano , Sara Mitri , Stephane Magnenat , and Laurent Keller Current Biology , vol. 17, no. 6, pp. 514-519, 2007. 2010. 05. 11 Jongwon Yoon. Contents. Introduction Evolution of multiagent systems in robotics

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2010. 05. 11 Jongwon Yoon

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  1. Evolutionary conditions for theemergence of communication in robotsDario Floreano, Sara Mitri, StephaneMagnenat, and Laurent KellerCurrent Biology, vol. 17, no. 6, pp. 514-519, 2007. 2010. 05. 11 Jongwon Yoon

  2. Contents • Introduction • Evolution of multiagent systems in robotics • Overview • Experimental setup • Robots • Foraging arena • Neural controller • Evolution process • Data analysis • Experimental results • Conclusion 1/13

  3. Introduction • Information transfer & communication systems • Plays a central role in the biology of most organisms, particularly social species • Extremely sophisticated in large and complex societies • Key component ensuring the ecological success of highly social species • Evolution of communication • Efficient communication requires tight coevolution between the signal emitted and the response elicited • Conditions and paths remain largely unknown • Contributions of this study • Predict about the evolutionary conditions conductive to the emergence of communication • Provide guidelines for designing artificial evolutionary systems 2/13

  4. Evolution of multiagent systems in robotics 3/13

  5. Overview • Purpose • Studying the evolution of communication • Consideration of the kin structure of groups (Relatedness) • The scale at which cooperation and competition occur (Level of selection) • Experiments overview • Colonies of robots forage in an environment • Containing a food and a poison • Use 100 colonies of 10 robots • Selection experiments over 500 generations • By using physics-based simulations 4/13

  6. Robots Experimental setup • Equipments • Two tracks : Independently rotate in both directions • Translucent ring : Emit blue light • 360 degree vision camera • Infrared ground sensors • Sensory-motor cycle • Length : 50ms • Use a neural controller to process visual information and ground-sensor input • Set direction and speed of the two tracks • Control the emission of blue light • Performance unit • Gain one unit : if it detected food • Lost one unit : if it detected poison • 1 Trial = 1200 sensory-motor cycles * 50ms = 1min 5/13

  7. Foraging arena Experimental setup • Size : 300cm x 300cm (Robots are placed randomly) • A food and a poison source • Radius : 10cm • Placed at 100cm from one of two opposite corners • Constantly emit red light • Circular gray and black papers • Placed under the food and the poison • Robots detect by infrared ground sensors 6/13

  8. Neural controller Experimental setup • Evolutionary Neural network • Feed-forward neural network • Ten inputs & three outputs • Genetic encoding • Encoded the synaptic weights of 30 neural connections • Each weight was encoded in 8bits, giving 256 values mapped onto the interval [-1, 1] • Total length : 8bits x 3 inputs x 10 outputs = 240 bits 7/13

  9. Evolutionary process Experimental setup • Population • 100 colonies x 10 robots in each colony = Total 1000 robots • 20 independent selection lines (replicates) • Selection • Four treatments • Colony-level / High relatedness • Individual-level / High relatedness • Colony-level / Low relatedness • Individual-level / High relatedness • Recombination • Crossover rate : 0.05 (5%) • Mutation rate : 0.01 (1%) 8/13

  10. Data analysis • Performance • Average performance of the 100 colonies over the last 50 generations • Compared with nonparametric (Kruskal-Wallis and Mann-Whitney) tests • Some of the data did not follow a normal distribution • Signaling strategy • NF / NP : Total number of cycles spent near the food / the poison • bFrn / bPrn : Whether robot r was emmiting light at cycle n near the food or poison • Tendency • The tendency of robots to be attracted by light • ar : Decrease in the distance as attraction • vr : Increase in the distance as avoidance 9/13

  11. Experimental results • Performance • Performance comparison 10/13

  12. Experimental results (cont.) • Strategy comparison • Produce light in the vicinity of the food : 12 / 20 • Produce light in the vicinity of the poison : 8 / 20 • The communication strategy where robots signaled near the food resulted in higher performance (259.6 ± 29.5) than the strategy of producing light near the poison (197.0 ± 16.8) • Signaling near the food while they feed • Food signal can easily be detected by other robots • Tendency comparison • Attracted to the light : 12 / 12 • Repelled by the light : 7 / 8 11/13

  13. Experimental results (cont.) 12/13

  14. Conclusion • Cooperative communication and deceptive signaling can evolve • Communication readily evolves when .. • Colonies consist of genetically similar individuals • Selection acts at the colony level • May constrain the evolution of more efficient communication system • Communication between signalers and receivers can be perturbed • Evolved biological systems can be maintained despite their suboptimal nature • Evolutionary principles are demonstrated • Can be useful for designing efficient groups of cooperative robots 13/13

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