Subdimensional Expansion for Multi-objective Multi-agent Path Finding

TL;DR

This paper introduces MOM*, a novel algorithm that efficiently computes the complete Pareto-optimal set for multi-objective multi-agent pathfinding by leveraging subdimensional expansion, overcoming the exponential growth of solution space.

Contribution

It extends subdimensional expansion to multi-objective pathfinding, creating MOM* that dynamically couples agents and efficiently finds Pareto-optimal solutions.

Findings

MOM* finds the complete Pareto set for complex instances.

It outperforms standard multi-objective A* in efficiency.

It scales to hundreds of solutions in large problem instances.

Abstract

Conventional multi-agent path planners typically determine a path that optimizes a single objective, such as path length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria may not be readily compared and sometimes lie in competition with each other. Simply applying standard multi-objective search algorithms to multi-agent path finding may prove to be inefficient because the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This paper presents an approach that bypasses this so-called curse of dimensionality by leveraging our prior multi-agent work with a framework called subdimensional expansion. One example of subdimensional expansion, when applied to A*, is called M* and M* was limited to a single objective function. We combine principles of dominance and subdimensional expansion to create a new algorithm named multi-objective M* (MOM*), which dynamically couples agents for planning only when those agents have to "interact" with each other. MOM* computes the complete Pareto-optimal set for multiple agents efficiently and naturally trades off sub-optimal approximations of the Pareto-optimal set and computational efficiency. Our approach is able to find the complete Pareto-optimal set for problem instances with hundreds of solutions which the standard multi-objective A* algorithms could not find within a bounded time.