Learning and stabilization of altruistic behaviors in multi-agent systems by reciprocity
This paper proposes a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. This strategy in conjunction with learning capabilities make altruistic agents cooperate only between them, thus preventing their exploitation by selfish agents, if future benefits are greater than the current cost of altruistic acts. This multi-agent system is made up of agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learn stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments.
Bibliographic Reference: Article: Biological Cybernetics (1998)
Record Number: 199810289 / Last updated on: 1998-03-09
Original language: en
Available languages: en