Collaborative Agents for Complex Problems Solving

Multi-Agent Systems (MAS) are particularly well suited to complex problem solving, whether the MAS comprises cooperative or competitive (selfinterested) agents. In this context we discuss both dynamic team formation among the former, as well as partner selection strategies with the latter type of agent. One-shot, long-term, and (fuzzy-based) flexible formation strategies are compared and contrasted, and experiments described which compare these strategies along dimensions of Agent Search Time and Award Distribution Situation.We find that the flexible formation strategy is best suited to self-interested agents in open, dynamic environments. Agent negotiation among competitive agents is also discussed, in the context of collaborative problem solving. We present a modification to Zhang’s Dual Concern Model which enables agents to make reasonable estimates of potential partner behavior during negotiation. Lastly, we introduce a Quadratic Regression approach to partner behavior analysis/estimation, which overcomes some of the limitations of Machine Learning-based approaches.