“相关推论”之原始创新论文

程京德

这是本人 1998 年的两篇论文，开创了“相关推论”这一新研究方向，是笔者研究室在这一研究方向上所有工作的初始基础。现将两篇论文存放于此，一方面留作保存记录，另一方面提供给听课学生和有兴趣者获取。

顺便把论文概要翻译为中文如下：

WCES-98 论文： “几乎所有当前的基于知识的系统都是直接或间接地基于经典数理逻辑的。经典数理逻辑不能保证推论的结论一定与其前提必然相关，即便该推论是符合经典数理逻辑的有效性标准的。正是这个问题导致了大型知识处理系统之推理和学习引擎的无效性和低效性。为了使基于知识的系统更加强大和灵活，我们必须从理论和实践两方面解决这个问题。本文提出，基于无悖论的相关逻辑的相关推理应被作为知识工程的逻辑基础。本文指出为什么基于古典数理逻辑的推论可能是不相关的，展示基于无悖论的相关逻辑的推论是相关的以及在相关推论中发挥根本作用的是归结蕴涵概念，并给出一些例子来说明为什么要把相关推论作为知识工程的逻辑基础。”

J. Cheng, “Relevant Reasoning as the Logical Basis of Knowledge Engineering,” in F. J. Cantu, R. Soto, J. Liebowitz, and E. Sucar (Eds.), “Application of Advanced Information Technologies: 4th World Congress on Expert Systems, Mexico City, Mexico, March 1998,” Vol. 1, pp. 449-457, Cognizant Communication Co., March 1998.

Abstract：“Almost all the current knowledge-based systems are directly or indirectly based on classical mathematical logic which gives no guarantee that the conclusion of a reasoningis necessarily relevant to its premises, even if the reasoning is valid in the sense of the classical mathematical logic.  It is this problem that causes the ineffectiveness and inefficiency of reasoning and learning engines of large-scale knowledge-based systems.  To make the current knowledge-based systems more powerful and flexible, we have to solve this problem from both theoretical and practical aspects.  This paper proposes that relevant reasoning based on paradox-free relevant logics should be taken as the logical basis of knowledge engineering.  The paper points out why a reasoning based on the classical mathematical logic may be irrelevant, shows that a reasoning based on the paradox-free relevant logics is relevant and that it is the notion of entailment that plays the fundamental role in relevant reasoning, and gives some examples to explain why one should take relevant reasoning as the logical basis of knowledge engineering.”

PDF: RR_WCES98.pdf

ICSMC-98 论文： “数据库中的知识发现（KDD）是从存储在数据库中的结构化数据中查找以前未知或未被识别和潜在有用的知识的过程。KDD的相关性问题是如何从可能包含与任务完全无关的知识的大量领域知识中选择与给定KDD任务相关的知识。基于强相关逻辑的相关推论可用于解决这一相关性问题。在本文中，我们提出了一种将领域知识库整合到KDD过程中的新的一般方法。此方法基于相关推论，模拟人们面对新旧情况时的思维方式。我们给出一个算法，用于从知识以产生式规则形式表达的领域知识库生成新的相关特征。”

K. A. Gouda and J. Cheng, “Using Relevant Reasoning to Solve the Relevancy Problem in Knowledge Discovery in Databases,” Proceedings of the 1998 IEEE Annual International Conference on Systems, Man, and Cybernetics, Vol. 2, pp. 1473-1478, San Diego, USA, The IEEE Systems, Man, and Cybernetics Society, October 1998.

Abstract：“Knowledge Discoveryin Databases (KDD) is a process to find previously unknown or unrecognized and potentially useful knowledge from structured data stored in databases.  The relevancy problem in KDD is  how to select the knowledge that is relevant to a given KDD task from a large body of domain knowledge that may contain knowledge irrelevant to the task at all.  Relevant reasoning based on strong relevant logic can be used to solve this relevancy problem. In this paper, we propose a new and general method to integrate domain knowledge bases into the KDD process. It is based on the relevant reasoning and simulates the human way of thinking when one faces a new or old situation.  We give an algorithm to create new relevant features from the domain knowledge bases where knowledge are represented in the form of production rules.”

PDF: RR_ICSMC98.pdf