Dominance Regions of Pursuit-evasion Games in Non-anticipative Information Patterns
Weiwen Huang, Li Liang, Ningsheng Xu, Fang Deng

TL;DR
This paper investigates the properties of the evader's dominance region in pursuit-evasion games under non-anticipative information patterns, establishing conditions for pursuer strategies to prevent evader escape, especially with obstacles.
Contribution
It rigorously proves the initial dominance region as the reachable region and provides conditions for pursuer strategies with and without obstacles, including a case with a corner obstacle.
Findings
Initial dominance region equals the evader's reachable region.
Existence of non-anticipative pursuer strategies depends on obstacle presence.
Counterexamples show strategies do not always exist with obstacles.
Abstract
The evader's dominance region is an important concept and the foundation of geometric methods for pursuit-evasion games. This article mainly reveals the relevant properties of the evader's dominance region, especially in non-anticipative information patterns. We can use these properties to research pursuit-evasion games in non-anticipative information patterns. The core problem is under what condition the pursuer has a non-anticipative strategy to prevent the evader leaving its initial dominance region before being captured regardless of the evader's strategy. We first define the evader's dominance region by the shortest path distance, and we rigorously prove for the first time that the initial dominance region of the evader is the reachable region of the evader in the open-loop sense. Subsequently, we prove that there exists a non-anticipative strategy by which the pursuer can capture…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · Game Theory and Applications · Complex Systems and Time Series Analysis
