用反应式合成解决多人路径规划的抓逃游戏，自动生成必胜策略。
Research Vision: Multi-Agent Path Planning for Cops And Robbers Via Reactive Synthesis
- 基于LTLt与协同合成，形式化定义抓逃游戏路径规划问题。
- 验证了多种游戏场景下警方策略的可实现性并生成可执行程序。
- 方法可扩展至其他反应式程序合成任务，具有通用性。
我们提出通过反应式合成解决经典抓逃游戏的多智能体路径规划问题。具体而言，利用LTLt与协同合成技术，旨在判断各类抓逃游戏是否可实现（即是否存在警方策略确保抓捕成功）。同时，我们将该策略构造成多个系统参与者可执行的程序。本文形式化了问题空间，并提出了潜在解决方案方向。此外，展示了该广义抓逃游戏的形式化如何映射到反应式程序合成领域的广泛问题。
原文摘要 · Abstract (English)
We propose the problem of multi-agent path planning for a generalization of the classic Cops and Robbers game via reactive synthesis. Specifically, through the application of LTLt and Coordination Synthesis, we aim to check whether various Cops and Robbers games are realizable (a strategy exists for the cops which guarantees they catch the robbers). Additionally, we construct this strategy as an executable program for the multiple system players in our games. In this paper we formalize the problem space, and propose potential directions for solutions. We also show how our formalization of this generalized cops and robbers game can be mapped to a broad range of other problems in the reactive program synthesis space.
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