First-Order Problem Solving through Neural MCTS based Reinforcement Learning. (arXiv:2101.04167v1 [cs.AI])

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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