Modeling cooperation in multi-agent communities ?
In Multi-Agent Systems the main goal is providing fruitful cooperation among agents in order to enrich the support given to user activities. Cooperation can be implemented in many ways, depending on how local knowledge of agents is represented and consists, in general, in providing the user with an integrated view of individual knowledge bases. But the main difficulty is determining which agents are promising candidates for a fruitful cooperation among the (possibly large) universe of agents operating in the net. This paper gives a contribution in this context, by proposing a formal framework for representing and managing cooperation in multi-agent networks. Semantic properties are here represented by coefficients and adaptive algorithms permit the computation of a set of agents suggested for cooperation. Actual choices of the users modify internal parameters in such a way that the next suggestions are closer to users expectancy.
multi-agent systems agent cooperation adaptive learning