Multiagent Stochastic Shortest Path Problem
Martin Jon\'a\v{s}, Anton\'in Ku\v{c}era, Vojt\v{e}ch K\r{u}r, Jan Ma\v{c}\'ak, Vojt\v{e}ch \v{R}eh\'ak
TL;DR
This paper introduces the multi-agent stochastic shortest path problem, analyzing its complexity and designing algorithms for strategy synthesis, with experimental evaluation on various instances.
Contribution
It formalizes the MSSP problem, studies its complexity, and provides efficient algorithms for strategy synthesis with experimental validation.
Findings
Algorithms effectively synthesize strategies for MSSP.
Complexity analysis reveals computational challenges.
Experimental results demonstrate scalability and performance.
Abstract
We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which 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.
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