000111358 001__ 111358
000111358 005__ 20190316234026.0
000111358 0247_ $$2doi$$a10.5075/epfl-thesis-3943
000111358 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis3943-8
000111358 02471 $$2nebis$$a5419993
000111358 037__ $$aTHESIS
000111358 041__ $$aeng
000111358 088__ $$a3943
000111358 245__ $$aEvolution of cooperation in artificial ants
000111358 269__ $$a2007
000111358 260__ $$aLausanne$$bEPFL$$c2007
000111358 300__ $$a174
000111358 336__ $$aTheses
000111358 520__ $$aThe evolution of cooperation is a fundamental and enduring puzzle in biology and the social sciences. Hundreds of theoretical models have been proposed, but empirical research has been hindered by the generation time of social organisms and by the difficulties of quantifying costs and benefits of cooperation. The significant increase in computational power in the last decade has made artificial evolution of simple social robots a promising alternative. This thesis is concerned with the artificial evolution of groups of cooperating robots. It argues that artificial evolution of robotic agents is a powerful tool to address open questions in evolutionary biology, and shows how insights gained from the study of artificial and biological multi-agent systems can be mutually beneficial for both biology and robotics. The work presented in this thesis contributes to biology by showing how artificial evolution can be used to quantify key factors in the evolution of cooperation in biological systems and by providing an empirical test of a central part of biological theory. In addition, it reveals the importance of the genetic architecture for the evolution of efficient cooperation in groups of organisms. The work also contributes to robotics by identifying three different classes of multi-robot tasks depending on the amount of cooperation required between team members and by suggesting guidelines for the evolution of efficient robot teams. Furthermore it shows how simulations can be used to successfully evolve controllers for physical robot teams.
000111358 6531_ $$aartificial evolution
000111358 6531_ $$amulti-agent systems
000111358 6531_ $$asocial insects
000111358 6531_ $$aevolutionary robotics
000111358 6531_ $$ateam composition
000111358 6531_ $$atask allocation
000111358 6531_ $$adivision of labor
000111358 6531_ $$afitness allocation
000111358 6531_ $$acooperation
000111358 6531_ $$aaltruism
000111358 700__ $$0241091$$aWaibel, Markus$$g146208
000111358 720_2 $$0240742$$aFloreano, Dario$$edir.$$g111729
000111358 720_2 $$aKeller, Laurent$$edir.
000111358 8564_ $$s2801959$$uhttps://infoscience.epfl.ch/record/111358/files/EPFL_TH3943.pdf$$yn/a$$zn/a
000111358 909C0 $$0252161$$pLIS$$xU10370
000111358 909CO $$ooai:infoscience.tind.io:111358$$pthesis$$pthesis-bn2018$$pDOI$$pSTI$$qDOI2$$qGLOBAL_SET
000111358 917Z8 $$x108898
000111358 917Z8 $$x108898
000111358 918__ $$aSTI$$bSTI-SMT$$cI2S
000111358 919__ $$aLIS
000111358 920__ $$b2007
000111358 970__ $$a3943/THESES
000111358 973__ $$aEPFL$$sPUBLISHED
000111358 980__ $$aTHESIS