Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
GA-Net: A Genetic Algorithm for Community Detection in Social Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior
IEEE Intelligent Systems
Experience-Driven Procedural Content Generation
IEEE Transactions on Affective Computing
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
We present a computational framework capable of inferring the existence of group identities, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial Societies (CAAS) by solely observing the flow of interactions occurring among the agents. Our modelling framework infers 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 provide 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.