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- W3120967653 abstract "The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement and the interpretation into a two-player semantic game. Many combinatorial problems can be described using interpreted FOL statements and can be mapped into a semantic game. Therefore, learning to play a semantic game perfectly leads to the solution of a specific instance of a combinatorial problem. We adapt the AlphaZero algorithm so that it becomes better at learning to play semantic games that have different characteristics than Go and Chess. We propose a general framework, Persephone, to map the FOL description of a combinatorial problem to a semantic game so that it can be solved through a neural MCTS based reinforcement learning algorithm. Our goal for Persephone is to make it tabula-rasa, mapping a problem stated in interpreted FOL to a solution without human intervention." @default.
- W3120967653 created "2021-01-18" @default.
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- W3120967653 date "2021-01-11" @default.
- W3120967653 modified "2023-09-27" @default.
- W3120967653 title "First-Order Problem Solving through Neural MCTS based Reinforcement Learning" @default.
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- W3120967653 doi "https://doi.org/10.48550/arxiv.2101.04167" @default.
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