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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.

Additional information

Authors: ZAMORA J, Departamento de Biomatemática, Universidad Complutense de Madrid (ES);MURCIANO A, Departamento de Biomatemática, Universidad Complutense de Madrid (ES);MILLÁN J, JRC Ispra (IT);MILLAN J, JRC Ispra (IT)
Bibliographic Reference: Article: Biological Cybernetics (1998)
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