Selective Exploration Algorithm for Coalition Formation

Abstract

Coalition formation with task dependencies has been the focus of few of the literature on multi-agent coalition formation. In contrast, very little attention has been given to the case where each agent has several alternative sets of tasks leading it to its goal satisfaction. The task dependencies impact the feasibility of the coalitions depending on the considered tasks alternative at a given time. Hence, the performance of a single task, whether by coalition formation (group of agents) or by a single performance, could affect the feasibility of other coalitions in the system. However, these task dependencies play a crucial role in many real-world multi-agent applications. Against this background, we consider in this paper multiple self-interested agents each of which has a goal it needs to achieve by performing a set of tasks. Each agent has several alternative sets of tasks leading it to its goal satisfaction. The tasks in an alternative exhibit dependencies and require sequential execution. So, to jointly achieve goals, the agents may form interdependent coalitions. We introduce a new algorithm that we call the Selective Exploration Algorithm (SEA) for coalition formation that accounts for the task dependencies to consider only feasible coalitions and reduce the size of the search space to explore and identify the optimal coalition structure.