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Multi-Objective Task Allocation for Multi-Agent Systems using Hierarchical Cost Function

Multi-agent systems are deployed to accomplish tasks that take a long time with a single agent. The task allocation problem becomes particularly difficult when the objectives are conflicting with one another (e.g. minimizing the mission time while respecting the task priorities, while simultaneously maximizing agent's fitness for the task). This paper presents an algorithm to create task assignments for a group of autonomous agents with competing objectives. We consider a variety of constraints including agent capabilities to perform tasks, priorities set by a human supervisor, as well as temporal constraints such as arrival time or coalition formation. We propose a multi-objective Particle Swarm Optimization (PSO) that uses a hierarchical cost function by leveraging the paradigm of lexicographic optimization. The particles are driven by higher ranked objectives with lower ranked objectives used to break ties. We demonstrate the effectiveness of this algorithm in a battlefield scenario where sub-teams of aerial vehicles are assigned to perform area reconnaissance, target strikes, and intelligence gathering.