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An efficient contradiction separation based automated deduction algorithm for enhancing reasoning capability

delete2023-02-01
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OA
AI
P
Peiyao Liu
S
Shuwei Chen *
刘
刘军 (Jun Liu)
Y
Yang Xu
F
Feng Cao
G
Guanfeng Wu
DOI:10.1016/j.knosys.2022.110217delete
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Abstract

Abstract

En 中文
Automated theorem prover (ATP) for first-order logic (FOL), as a significant inference engine, is one of the hot research areas in the field of knowledge representation and automated reasoning. E prover, as one of the leading ATPs, has made a significant contribution to the development of theorem provers for FOL, particularly equality handling, after more than two decades of development. However, there are still a large number of problems in the TPTP problem library, the benchmark problem library for ATPs, that E has yet to solve. The standard contradiction separation (S-CS) rule is an inference method introduced recently that can handle multiple clauses in a synergized way and has a few distinctive features which complements to the calculus of E. Binary clauses, on the other hand, are widely utilized in the automated deduction process for FOL because they have a minimal number of literals (typically only two literals), few symbols, and high manipulability. As a result, it is feasible to improve a prover's deduction capability by reusing binary clause. In this paper, a binary clause reusing algorithm based on the S-CS rule is firstly proposed, which is then incorporated into E with the objective to enhance E's performance, resulting in an extended E prover. According to experimental findings, the performance of the extended E prover not only outperforms E itself in a variety of aspects, but also solves 18 problems with rating of 1 in the TPTP library, meaning that none of the existing ATPs are able to resolve them. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Knowledge representation
Automated reasoning
First-order logic
Automated theorem prover
Standard contradiction rule
E prover
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Journal

K
Knowledge-Based Systems
IF:
7.6
Papers:
1.3W
Citations:
4.5W

Organization

U
Ulster University
Scholars:
5.7K
Papers: 5.9K
Citations: 25
S
Southwest Jiaotong University
Scholars:
2.9W
Papers: 2.1W
Citations: 2.3W
J
jiangxi university of science & technology
Scholars:
6.7K
Papers: 4.5K
Citations: 3
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