Symbolic LTLf Best-Effort Synthesis
This work addresses a specific challenge in automated planning and synthesis for agents in uncertain environments, representing an incremental improvement over existing methods.
The paper tackles the problem of synthesizing strategies for agents in nondeterministic environments when a guaranteed solution is impossible, proposing symbolic approaches for best-effort synthesis in Linear Temporal Logic on finite traces (LTLf). It compares different combinations of these approaches, showing significant performance variations in empirical evaluations.
We consider an agent acting to fulfil tasks in a nondeterministic environment. When a strategy that fulfills the task regardless of how the environment acts does not exist, the agent should at least avoid adopting strategies that prevent from fulfilling its task. Best-effort synthesis captures this intuition. In this paper, we devise and compare various symbolic approaches for best-effort synthesis in Linear Temporal Logic on finite traces (LTLf). These approaches are based on the same basic components, however they change in how these components are combined, and this has a significant impact on the performance of the approaches as confirmed by our empirical evaluations.