The effects of co-operation on multiagent search in task-oriented domains

This paper studies the benefits of teaming and selflessness when using multiagent search to solve task-oriented problems. A formal framework for multiagent search is presented, which forms a superset of the task-oriented domain, coalition formation, distributed constraint satisfaction and NK landscape search problems. The paper focuses on task-oriented domain problems and shows how the benefits of teaming and selflessness arise in this domain. These experimental results are compared to similar results in the NK domain—from which a predictive technique is imported. Namely, it is shown that better allocations are found when the dynamics of the multiagent system lie between order and chaos. Several other specific findings are presented such as the fact that neither absolute selfishness nor absolute selflessness result in better allocations, and the fact that the formation of small teams usually leads to better allocations.