Constraint Learning for Non-confluent Proof Search
🤖 AI Summary
This work addresses the inefficiency of non-confluent tableau calculi—such as connection tableaux—in proof search, where frequent backtracking severely hampers performance, and naive backtracking restrictions compromise completeness. For the first time, the authors integrate a constraint learning mechanism into the classical first-order connection calculus, substantially reducing backtracking while preserving theoretical completeness. The core innovation lies in a novel, iteratively refinable constraint language that dynamically prunes and optimizes the search space. Experimental results demonstrate a significant improvement in practical proof efficiency. Moreover, the proposed framework offers a general and transferable approach to backtracking control, applicable to other non-confluent tableau systems.
Application Category
📝 Abstract
Proof search in non-confluent tableau calculi, such as the connection tableau calculus, suffers from excess backtracking, but simple restrictions on backtracking are incomplete. We adopt constraint learning to reduce backtracking in the classical first-order connection calculus, while retaining completeness. An initial constraint learning language for connection-driven search is iteratively refined to greatly reduce backtracking in practice. The approach may be useful for proof search in other non-confluent tableau calculi.
Problem
Research questions and friction points this paper is trying to address.
proof search
non-confluent tableau
backtracking
constraint learning
connection calculus
Innovation
Methods, ideas, or system contributions that make the work stand out.
constraint learning
non-confluent tableau
proof search
connection calculus
backtracking reduction
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