First-Order Problem Solving through Neural MCTS based Reinforcement Learning

• URL: http://arxiv.org/abs/2101.04167v1
• Date: Mon, 11 Jan 2021 19:54:06 GMT
• Title: First-Order Problem Solving through Neural MCTS based Reinforcement Learning
• Authors: Ruiyang Xu, Prashank Kadam, Karl Lieberherr
• Abstract summary: Many problems can be described using interpreted FOL statements and can be mapped into a semantic game. We propose a general framework, Persephone, to map the FOL description of a problem to a semantic game. Our goal for Persephone is to make it tabula-rasa, mapping a problem stated in interpreted FOL to a solution without human intervention.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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