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.

翻译：我们考虑一个在非确定性环境中执行任务以满足目标的智能体。当不存在一种无论环境如何行动都能确保任务完成的策略时，智能体至少应避免采用会阻碍任务完成的策略。尽力合成（best-effort synthesis）捕捉了这一直觉。在本文中，我们设计并比较了有限迹线性时序逻辑（LTLf）中尽力合成的多种符号化方法。这些方法基于相同的基本组件，但在组件的组合方式上有所不同，而我们的实证评估证实，这种差异对方法的性能有显著影响。