Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences

• URL: http://arxiv.org/abs/2510.07276v2
• Date: Sun, 12 Oct 2025 20:46:01 GMT
• Title: Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
• Authors: Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian,
• Abstract summary: Multi-objective path finding (MO-MAPF) algorithms produce conflict-free plans.<n>We propose a lexicographic framework for modeling MO-MAPF, along with an algorithm textitLexicographic Conflict-Based Search (LCBS)<n>LCBS integrates a priority-aware low-level $A*$ search with conflict-based search.<n>We provide insights into optimality and scalability, and empirically demonstrate that LCBS computes optimal solutions while scaling to instances with up to ten objectives -- far beyond the limits of existing MO-MAPF methods.
• Score: 7.18523391773903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi-objective multi-agent path finding (MO-MAPF) algorithms typically produce conflict-free plans by computing Pareto frontiers. They do not explicitly optimize for user-defined preferences, even when the preferences are available, and scale poorly with the number of objectives. We propose a lexicographic framework for modeling MO-MAPF, along with an algorithm \textit{Lexicographic Conflict-Based Search} (LCBS) that directly computes a single solution aligned with a lexicographic preference over objectives. LCBS integrates a priority-aware low-level $A^*$ search with conflict-based search, avoiding Pareto frontier construction and enabling efficient planning guided by preference over objectives. We provide insights into optimality and scalability, and empirically demonstrate that LCBS computes optimal solutions while scaling to instances with up to ten objectives -- far beyond the limits of existing MO-MAPF methods. Evaluations on standard and randomized MAPF benchmarks show consistently higher success rates against state-of-the-art baselines, especially with increasing number of objectives.

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