文章基本信息

标题：Abstraction Refinement for Games with Incomplete Information
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作者：Rayna Dimitrova ; Bernd Finkbeiner
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2008
卷号：2
页码：175-186
DOI：10.4230/LIPIcs.FSTTCS.2008.1751
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：Counterexample-guided abstraction refinement (CEGAR) is used in automated software analysis to find suitable finite-state abstractions of infinite-state systems. In this paper, we extend CEGAR to games with incomplete information, as they commonly occur in controller synthesis and modular verification. The challenge is that, under incomplete information, one must carefully account for the knowledge available to the player: the strategy must not depend on information the player cannot see. We propose an abstraction mechanism for games under incomplete information that incorporates the approximation of the players\' moves into a knowledge-based subset construction on the abstract state space. This abstraction results in a perfect-information game over a finite graph. The concretizability of abstract strategies can be encoded as the satisfiability of strategy-tree formulas. Based on this encoding, we present an interpolation-based approach for selecting new predicates and provide sufficient conditions for the termination of the resulting refinement loop.
关键词：Automatic abstraction refinement; synthesis