Enhancing mas cooperative search through coalition partitioning

This paper presents new search strategies for agents with diverse preferences searching cooperatively (i.e., as a coalition) in complex environments where searching is a costly activity. The uniqueness of our proposed mech-anism is in the ability of the coalition to partition itself into sub-coalitions that continue the search autonomously (a capability neglected in earlier cooperative search models). As we show throughout the paper, this strategy is always favorable in comparison to cooperative and autonomous search techniques currently in use. The paper presents a comprehensive analysis of the new cooperative search method and highlights the unique character-istics of the optimal search strategy in such a search model. Furthermore, for many common environments we manage to eliminate the consequential added computational complexity associated with the partitioning option by introducing innovative efficient algorithms for extracting the coalition’s optimal search strategy