TY - GEN
T1 - Interaction-based group identity detection via reinforcement learning and artificial evolution
AU - Grappiolo, Corrado
AU - Togelius, Julian
AU - Yannakakis, Georgios N.
PY - 2013
Y1 - 2013
N2 - We present a computational framework capable of inferring the existence of group identities, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial So- cieties (CAAS) by solely observing the flow of interactions occurring among the agents. Our modelling framework in- fers the group identities by following two steps: first, it aims to learn the ongoing levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learned cooperation values, to partition the agents into groups and assign group identities to the agents. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum (or Bargain) Social Dilemma Game, show that a cooperation learning phase, based on Reinforcement Learning, can pro- vide highly promising results for minimising the mismatch between the existing and the inferred group identities. The proposed method appears to be robust independently of the size and the ongoing social dynamics of the societies.
AB - We present a computational framework capable of inferring the existence of group identities, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial So- cieties (CAAS) by solely observing the flow of interactions occurring among the agents. Our modelling framework in- fers the group identities by following two steps: first, it aims to learn the ongoing levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learned cooperation values, to partition the agents into groups and assign group identities to the agents. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum (or Bargain) Social Dilemma Game, show that a cooperation learning phase, based on Reinforcement Learning, can pro- vide highly promising results for minimising the mismatch between the existing and the inferred group identities. The proposed method appears to be robust independently of the size and the ongoing social dynamics of the societies.
KW - Adaptive artificial societies
KW - Evolutionary com- putation
KW - Group identity detection
KW - Reinforcement learning
KW - Ul- timatum game
UR - http://www.scopus.com/inward/record.url?scp=84882423713&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84882423713&partnerID=8YFLogxK
U2 - 10.1145/2464576.2482722
DO - 10.1145/2464576.2482722
M3 - Conference contribution
AN - SCOPUS:84882423713
SN - 9781450319645
T3 - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
SP - 1423
EP - 1430
BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
T2 - 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
Y2 - 6 July 2013 through 10 July 2013
ER -