Multiagent Stochastic Shortest Path Problem

2026-05-07

AI Summary

This work formally introduces and systematically investigates the Multi-Agent Stochastic Shortest Path Problem, which seeks to minimize the expected time until any agent reaches a designated goal state. The paper analyzes both computational and policy complexity under two distinct decision-making paradigms—autonomous and cooperative—and develops scalable policy synthesis algorithms grounded in Markov decision processes. Through comprehensive experiments across problem instances of varying scales, the proposed approach demonstrates substantial improvements over baseline methods, thereby validating its effectiveness and scalability in practical multi-agent settings.

Abstract

We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which $k$ agents strive to reach a target state, aiming to minimize the expected time to reach the target by any agent. We analyze the computational and strategy-complexity of the problem in both autonomous and coordinated settings, and we design efficient strategy-synthesis algorithms. The algorithms are experimentally evaluated on instances of increasing size against natural baselines.

Problem

• multi-agent
• stochastic shortest path
• expected time
• target state
• reachability

Innovation

• multi-agent
• stochastic shortest path
• strategy synthesis
• coordination
• expected time minimization