A hierarchical conflict resolution method for multi-agent path planning

Authors:
Kuang-Yuan Chen;Peter A. Lindsay;Peter J. Robinson;Hussein A. Abbass
Affiliations:
ARC Centre for Complex Systems, The University of Queensland, Australia;ARC Centre for Complex Systems, The University of Queensland, Australia;ARC Centre for Complex Systems, The University of Queensland, Australia;ARC Centre for Complex Systems, University of New South Wales, Australia
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

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.