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 , Jongwon Yoon

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

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

Evolution of multiagent systems in robotics AuthorTargetYear Team compositionLevel of selection Hetero -geneous Homo -geneous IndividualTeam S. Raik and B. DurnotaBehavior1994OO S. Luke and L. SpectorBehavior1996OO S. G. Ficicici et al.Behavior1999OO A. S. Wu et al.Behavior1999OO A. MartinoliBehavior1999OO M. QuinnBehavior2001OOO E. Simoes and D. BaroneBehavior2002OO L. SteelsCommunication2003OO L. Spector et al.Behavior2005OO M. Mirolli and D. ParisiCommunication2005OO V. Trianni et al.Communication2006OO 3/13

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

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

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

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

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

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 –N F / N P : Total number of cycles spent near the food / the poison –b F rn / b P rn : Whether robot r was emmiting light at cycle n near the food or poison Tendency –The tendency of robots to be attracted by light –a r : Decrease in the distance as attraction –v r : Increase in the distance as avoidance 9/13

Experimental results Performance Performance comparison 10/13

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

Experimental results (cont.) 12/13

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