A hierarchical conflict resolution method for multi-agent path planning

Prioritisation is an important technique for resolving planning conflicts between agents with shared resources, such as robots moving through a shared space. This paper explores the use of genetic-based machine learning to assign priority dynamically, to improve performance of a team of agents without unduly impacting individual agents' performance. A decoupled heuristic approach is used for flexibility, whereby individual XCS agents learn to optimise their behaviour first, and then a high-level planner agent is introduced and trained to resolve conflicts by assigning priority. The approach is designed for Partially Observable Markov Decision Process (POMDP) environments and demonstrated on a problem in 3D aircraft path planning.