Decentralized Multi-Agent Strategy Synthesis under LTLf Specifications via Exchange of Least-Limiting Advisers

We propose a decentralized solution to a high-level task-planning problem for a multi-agent system under a set of possibly dependent LTL
$f$
specifications. We propose an approach where the problem is turned into a number of individual two and a half player stochastic games with reachability objectives. If almost-surely winning strategies cannot be found for them, we deploy so-called least-limiting advisers to restrict agents' behaviours. A key step is treating safety and liveness separately, by synthesizing necessary safety and fairness assumptions and iteratively exchanging them in the form of advisers between the agents. We avoid the state-space explosion problem by computing advisers locally in each game, independently of the model and specification of other agents. The solution is sound, but conservative. We demonstrate its scalability in a series of simulated scenarios involving cleaning of an office-like environment.