Symbolic LTLf Best-Effort Synthesis

TL;DR

This paper explores symbolic methods for best-effort synthesis in LTLf, enabling agents to avoid strategies that hinder task fulfillment in nondeterministic environments, with empirical evaluation showing performance differences.

Contribution

It introduces and compares various symbolic approaches for best-effort synthesis in LTLf, highlighting how component combinations affect efficiency.

Findings

Different approaches significantly impact performance.

Empirical evaluations confirm the effectiveness of the methods.

Strategies to avoid hindering task fulfillment are feasible.

Abstract

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.