This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, where the nondeterminism is an adversarial player whose objective is to prevent achieving temporal and reachability goals. The aim is to synthesize a winning strategy -- a reactive (robust) strategy that guarantees the satisfaction of the goals under all possible moves of the adversarial player. Our proposed approach involves growing a (search) game-tree in the hybrid space by combining sampling-based motion planning with a novel bandit-based technique to select and improve on partial strategies. We show that the algorithm is probabilistically complete, i.e., the algorithm will asymptotically almost surely find a winning strategy, if one exists. The case studies and benchmark results show that our algorithm is general and effective, and consistently outperforms state of the art algorithms.

翻译：本文提出一种面向具有复杂连续动态且受时间与可达性约束的非确定性混合系统的基于采样策略综合算法。我们将混合系统演化为双人博弈模型，其中非确定性因素作为对抗性玩家存在，其目标在于阻止系统达成时间与可达性目标。研究旨在综合出获胜策略——一种能保证在对抗性玩家所有可能行动下仍满足目标的反应式（鲁棒）策略。所提方法通过将基于采样的运动规划与新型基于置信上界的部分策略选择改进技术相结合，在混合空间中构建（搜索）博弈树。算法具有概率完备性：若获胜策略存在，该算法将以渐近概率1找到该策略。案例研究与基准测试结果表明，本算法具有通用性与有效性，并持续优于现有最优算法。