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 1953 Kuhn introduced extensive-form games of imperfect information, including the distinction and connection between mixed and behavioral strategies. [Kuhn, ...
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 ...
Jul 26, 2024 · Abstract. We consider two-player games with imperfect information and the synthesis of a randomized strategy.
Competitive imperfect information games where the goal is to maximally exploit an unknown opponent's weaknesses are an example of this problem. Agents for these ...