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 selection

Hetero-geneous

Homo-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 5/13

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

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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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