LTLf Best-Effort Synthesis in Nondeterministic Planning Domains
This work addresses the challenge of planning under uncertainty for agents in nondeterministic environments, offering a more scalable solution but is incremental as it builds on existing best-effort synthesis concepts.
The paper tackles the problem of synthesizing best-effort strategies in fully observable nondeterministic domains for LTLf goals, where no guaranteed strategy exists, by presenting a game-theoretic technique that improves scalability compared to a direct approach.
We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications.