基于矛盾分离的自动推理的协同子句选择策略

Shuwei Chen, Yang Xu, Yan Jiang, Jun Liu, Xingxing He
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引用次数: 4

摘要

基于协同动态矛盾分离的自动演绎理论提供了一种新的基于逻辑的自动演绎推理框架,将静态二元解析推理规则扩展为基于动态多重矛盾分离的自动演绎机制。这种基于矛盾分离的自动推理机制具有动态性、多子句涉及性、协同性、目标导向性和鲁棒性等特点。为了进一步提高这种新型自动推理机制的效率和可行性,本文提出了自动推理过程中分句或文字选择的策略,主要考虑了多分句在自动推理过程中的协同效应。通过实例说明了这些策略的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Some synergized clause selection strategies for contradiction separation based automated deduction
The synergized dynamic contradiction separation based automated deduction theory has provided a novel logic based automated deduction reasoning framework, which ex­tends the static binary resolution inference rule to a dynamic multiple contradiction separation based automated deduction mechanism. This novel contradiction separation based auto­mated deduction mechanism is characterized as a dynamic, multi-clauses involving, synergized, goal-oriented and robust automated reasoning framework. In order to further improve the efficiency and feasibility of this novel automated deduction mechanism, this paper proposes some strategies for clause or literal selection during the automated deduction process, which consider mainly the synergized effect of multi-clauses during the deduction process. Some examples are put forward to illustrate the feasibility of these proposed strategies.
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