2010. 05. 11 jongwon yoon
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Evolutionary conditions for theemergence of communication in robots
Dario Floreano, Sara Mitri, Stephane Magnenat, and Laurent KellerCurrent Biology, vol. 17, no. 6, pp. 514-519, 2007.
2010. 05. 11Jongwon Yoon
Contents
• Introduction• Evolution of multiagent systems in robotics• Overview• Experimental setup
– Robots– Foraging arena– Neural controller– Evolution process
• Data analysis• Experimental results• Conclusion
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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
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Evolution of multiagent systems in ro-botics
Author Target Year
Team composition Level of selectionHetero
-geneousHomo
-geneous Individual Team
S. Raik and B. Durnota Behavior 1994 O O
S. Luke and L. Spector Behavior 1996 O O
S. G. Ficicici et al. Behavior 1999 O O
A. S. Wu et al. Behavior 1999 O O
A. Martinoli Behavior 1999 O O
M. Quinn Behavior 2001 O O O
E. Simoes and D. Barone Behavior 200
2 O O
L. Steels Communica-tion
2003 O O
L. Spector et al. Behavior 2005 O O
M. Mirolli and D. Parisi Communica-tion
2005 O O
V. Trianni et al. Communica-tion
2006 O O
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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
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Robots Experimental setup
• Equipments– Two tracks : Independently rotate in both direc-
tions– Translucent ring : Emit blue light– 360 degree vision camera– Infrared ground sensors
• Sensory-motor cycle– Length : 50ms
• Use a neural controller to process visual informa-tion 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
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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
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Neural controller Experimental setup
• Evolutionary Neural network– Feed-forward neural network– Ten inputs & three outputs
• Genetic encoding– Encoded the synaptic weights of 30 neu-
ral connections– Each weight was encoded in 8bits, giv-
ing 256 values mapped onto the interval [-1, 1]
– Total length : 8bits x 3 inputs x 10 out-puts
= 240 bits
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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%)
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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– bF
rn / 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
Experimental results• Performance
• Performance comparison
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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 re-
sulted in higher performance (259.6 ± 29.5) than the strategy of produc-ing 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
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Experimental results (cont.)
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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 sys-tem
– 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
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