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Connections: Markov Decision Processes for Classical, Intuitionistic, and Modal Connection Calculi
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Abstract
This paper introduces a framework for integrating Reinforcement Learning (RL) with proof search in connection calculi for classical, intuitionistic, and modal logic. We specify a mapping from the relevant connection calculi to Markov Decision Processes (MDPs), and provide a Python library implementing such MDPs.
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CEUR Workshop Proceedings
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International Workshop on Automated Reasoning with Connection Calculi (AReCCa)
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1613-0073
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Except where otherwised noted, this item's license is described as Attribution 4.0 International