Evolving Strategies for Social Innovation Games

Erkin Bahçeci, Riitta Katila, Risto Miikkulainen
2015 Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15  
While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more e↵ectively in competitive problem-solving domains. This paper formalizes human creative problem solving as competitive multiagent search, and advances the hypothesis that evolutionary computation can be used to discover e↵ective strategies for it. In experiments in a social innovation game (similar to a fantasy sports
more » ... e), neural networks were first trained to model individual human players. These networks were then used as opponents to evolve better game-play strategies with the NEAT neuroevolution method. Evolved strategies scored significantly higher than the human models by innovating, retaining, and retrieving less and by imitating more, thus providing insight into how performance could be improved in such domains. Evolutionary computation in competitive multi-agent search thus provides a possible framework for understanding and supporting various human creative activities in the future.
doi:10.1145/2739480.2754790 dblp:conf/gecco/BahceciKM15 fatcat:xanhbgndkzhanmfrdcmbrx25gi