基于定理综合的直觉命题逻辑中的溯因推理

IF 1.4 2区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Theory and Practice of Logic Programming Pub Date : 2022-05-11 DOI:10.1017/S1471068422000254
Paul Tarau
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引用次数: 2

摘要

摘要利用直觉命题逻辑的一个紧凑的基于prolog的定理证明,综合了给定公式成为定理的最小假设。在应用我们的综合算法来覆盖基本的溯因推理机制之后,我们综合了模拟经典逻辑或中间逻辑中真值表行的文字连词,并推导了将经典逻辑或中间逻辑的定理转化为直觉逻辑中的定理的条件假设。进一步,我们推广了我们的溯因推理机制,以使用规范公式的最小集来合成更具表达性的顺序前提,从而可以将微积分中的任意公式简化为保留其可证明性。组织作为一个独立的识字Prolog程序,论文支持其内容的交互式探索,并确保我们的结果的完全可复制性。
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Abductive Reasoning in Intuitionistic Propositional Logic via Theorem Synthesis
Abstract With help of a compact Prolog-based theorem prover for Intuitionistic Propositional Logic, we synthesize minimal assumptions under which a given formula formula becomes a theorem. After applying our synthesis algorithm to cover basic abductive reasoning mechanisms, we synthesize conjunctions of literals that mimic rows of truth tables in classical or intermediate logics and we abduce conditional hypotheses that turn the theorems of classical or intermediate logics into theorems in intuitionistic logic. One step further, we generalize our abductive reasoning mechanism to synthesize more expressive sequent premises using a minimal set of canonical formulas, to which arbitrary formulas in the calculus can be reduced while preserving their provability. Organized as a self-contained literate Prolog program, the paper supports interactive exploration of its content and ensures full replicability of our results.
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来源期刊
Theory and Practice of Logic Programming
Theory and Practice of Logic Programming 工程技术-计算机:理论方法
CiteScore
4.50
自引率
21.40%
发文量
40
审稿时长
>12 weeks
期刊介绍: Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
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