Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding


  • Shuwei Wang University of Alberta
  • Vadim Bulitko University of Alberta
  • Taoan Huang University of Southern California
  • Sven Koenig University of Southern California
  • Roni Stern Ben Gurion University of the Negev



Multi-agent Pathfinding, Prioritized Planning, Program Synthesis


Prioritized planning is a popular approach to multi-agent pathfinding. It prioritizes the agents and then repeatedly invokes a single-agent pathfinding algorithm for each agent such that it avoids the paths of higher-priority agents. Performance of prioritized planning depends critically on cleverly ordering the agents. Such an ordering is provided by a priority function. Recent work successfully used machine learning to automatically produce such a priority function given good orderings as the training data. In this paper we explore a different technique for synthesizing priority functions, namely program synthesis in the space of arithmetic formulae. We synthesize priority functions expressed as arithmetic formulae over a set of meaningful problem features via a genetic search in the space induced by a context-free grammar. Furthermore we regularize the fitness function by formula length to synthesize short, human-readable formulae. Such readability is an advantage over previous numeric machine-learning methods and may help explain the importance of features and how to combine them into a good priority function for a given domain. Moreover, our experimental results show that our formula-based priority functions outperform existing machine-learning methods on the standard benchmarks in terms of success rate, run time and solution quality without using more training data.




How to Cite

Wang, S., Bulitko, V., Huang, T., Koenig, S., & Stern, R. (2023). Synthesizing Priority Planning Formulae for Multi-Agent Pathfinding. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 360-369.