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CEDAR (Counter Example Driven Antichain Refinement) is a new symbolic algorithm for computing weakest strategies for safety games of im- perfect information.
cedar (Counter Example Driven Antichain Refinement) is a new symbolic algorithm for computing weakest strategies for safety games of imperfect information.
CEDAR (Counter Example Driven Antichain Refinement) is a new symbolic algorithm for computing weakest strategies for safety games of im- perfect information.
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#game studies · #safety · Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
We examine three heuristic algorithms for games with imperfect information: Monte-carlo sampling, and two new algorithms we call vector minimaxing and payoff-.
In two player games, a matrix shows the payoffs as a mapping of the strategies of each player. If three players are involved, more than one matrix is involved, ...
Jul 26, 2024 · Abstract. We consider two-player games with imperfect information and the synthesis of a randomized strategy.
To learn a decent strategy in imperfect information games with long histories or loops, such as Geister, we propose a new method on top of CFR and its state ...
Computing Weakest Strategies for Safety Games of Imperfect Information · W. KuijperJ. Pol. Computer Science. International Conference on Tools and Algorithms ...