为不确定环境中的多层级目标设计可自适应策略，动态响应环境合作。
LTLf Adaptive Synthesis for Multi-Tier Goals in Nondeterministic Domains
- 基于博弈论构建自适应策略，动态优先满足可达成目标。
- 算法时间复杂度为二次方，仅比标准LTLf合成略高。
- 适合需分层应对不确定性的智能体系统设计者。
我们研究了一种LTLf合成的变体，旨在非确定性规划域中为多层级目标生成自适应策略，该目标包含多个逐步提升难度的LTLf目标。自适应策略在执行过程中（i）尽可能满足多层级目标中的已实现目标，（ii）利用环境可能的合作来推动剩余目标的达成。这一过程是动态的：若环境配合使某目标变得可强制实现，则策略将立即启动对该目标的强制。本文提出一种博弈论方法计算此类策略，保证正确性和完备性，且时间复杂度为关于目标数量的二次多项式，相比标准LTLf合成仅带来轻微开销。
原文摘要 · Abstract (English)
We study a variant of LTLf synthesis that synthesizes adaptive strategies for achieving a multi-tier goal, consisting of multiple increasingly challenging LTLf objectives in nondeterministic planning domains. Adaptive strategies are strategies that at any point of their execution (i) enforce the satisfaction of as many objectives as possible in the multi-tier goal, and (ii) exploit possible cooperation from the environment to satisfy as many as possible of the remaining ones. This happens dynamically: if the environment cooperates (ii) and an objective becomes enforceable (i), then our strategies will enforce it. We provide a game-theoretic technique to compute adaptive strategies that is sound and complete. Notably, our technique is polynomial, in fact quadratic, in the number of objectives. In other words, it handles multi-tier goals with only a minor overhead compared to standard LTLf synthesis.
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