对Etherington并行网络推理算法不完整性问题的探讨

网络缺省推理中扩展的计算需要进行并行性处理．Ｅｔｈｅｒｉｎｇｔｏｎ的并行网络推理算法（ＰＮＩＡ）为缺省推理提供了一条有效的途径．但它存在不能穷尽所有扩展的不完整性问题．本文提出了一个解决这一不完整性间题的并行网络推理算法．     

代数递归逻辑与人工智能

融合代数和递归函数理论,本文建了一个可以刻画知识表示、智能获取和自动推理的逻辑理论,给出了从示例中获得取知识的多项式复杂性算法和自动推理多项式复杂性算法。作为代数递归逻辑的应用,本文给出了人工智能中的约束满足问题的处理方案,探讨了人工智能的基本问题。     

正则无析取缺省理论

知识表示和推理一直是人工智能领域中的一个非常重要的研究课题。早在1958年，McCharthy就提倡用逻辑方法来研究知识表示和推理以达到人工智能的目的。Reiter的缺省逻辑就是最有效的工具之一，这是因为它能够很好地刻画和处理不完全信息下的推理。然而，缺省逻辑并不是完美无缺的。首先，有的缺省理论没有扩充。这样的缺省理论是没有意义的。其次，缺省推理具有高难度。在一般情况下，缺省推理处在复杂性分层的第二层上。即使对于disjunction-free缺省逻辑，它的复杂性也是NP-完全的。     

子句型缺省理论的推理算法

1 引言缺省理论自1980年Reiter提出之后,已成为非单调推理的热点。在缺省逻辑中,扩张的概念至为重要。Reiter对特殊的缺省理论——正规缺省理论做了许多研究,并得出了一些漂亮的结果。Etherington给出了生成任意有穷有序半正规缺省理论的扩张的程序。张明义提出缺省的一种子类——自相容缺省理     

通用对弈游戏:一个探索机器游戏智能的领域

通用对弈游戏（general game playing,简称GGP）是致力于提高机器的通用游戏智能的研究领域.与专用游戏智能程序不同,GGP玩家直到游戏开始时才获得游戏规则,从而避免依赖于人类关于特定游戏的经验.GGP研究发展至今,已在游戏表示、搜索算法、状态估值等方面做了深入探索,并在知识迁移等方面做出了尝试.GGP研究的进展在一定程度上代表了通用人工智能的发展,因而是值得关注的.     

关于解释学习逻辑构架的一些扩充

近来,在机器学习研究领域中,解释学习倍受重视,而其中心机制——解释推广却受限于一阶逻辑,为支持高阶对象和模态概念,本文介绍了两种扩充 Horn 逻辑表示域的方法,并通过例子说明了解释推广在扩充表示域中的实现。     

间断区间时态逻辑的语义

区间逻辑不能模拟自然语言中与,或,非时态关系,其公理系统的完备性不易保证。我们建立的间断区间时态知脚注可以克服区间逻辑的上述缺点,本文给出了间断区间逻辑的语法,语义及公理,即描述了间断区间时态逻辑的语义。     

缺省推理中的三个定理

本文证明了缺省推理中的三个定理.定理1表明了缺省推理的非单调性这一特点.定理2的实际意义在于,在一个封闭规范缺省理论(D,W)中,只要W能推出D中某些缺省的结论,则可以把这样的缺省规则从理论中删除,所得到的较小的缺省理论其延伸仍与原来缺省理论一样.尤其是若W能推出D中所有的缺省规则结论,则(D,W)的延伸就是W,这就是本文推论的结论.     

Embedding defaults into terminological knowledge representation formalisms

We consider the problem of integrating Reiter's default logic into terminological representation systems. It turns out that such an integration is less straightforward than we expected, considering the fact that the terminological language is a decidable sublanguage of first-order logic. Semantically, one has the unpleasant effect that the consequences of a terminological default theory may be rather unintuitive, and may even vary with the syntactic structure of equivalent concept expressions. This is due to the unsatisfactory treatment of open defaults via Skolemization in Reiter's semantics. On the algorithmic side, we show that this treatment may lead to an undecidable default consequence relation, even though our base language is decidable, and we have only finitely many (open) defaults. Because of these problems, we then consider a restricted semantics for open defaults in our terminological default theories: default rules are applied only to individuals that are explicitly present in the knowledge base. In this semantics it is possible to compute all extensions of a finite terminological default theory, which means that this type of default reasoning is decidable. We describe an algorithm for computing extensions and show how the inference procedures of terminological systems can be modified to give optimal support to this algorithm.This is a revised and extended version of a paper presented at the3rd International Conference on Principles of Knowledge Representation and Reasoning, October 1992, Cambridge, MA.     

Autoepistemic logic of first order and its expressive power

We study the expressive power of first-order autoepistemic logic. We argue that full introspection of rational agents should be carried out by minimizing positive introspection and maximizing negative introspection. Based on full introspection, we propose the maximal well-founded semantics that characterizes autoepistemic reasoning processes of rational agents, and show that breadth of the semantics covers all theories in autoepistemic logic of first order, Moore's AE logic, and Reiter's default logic. Our study demonstrates that the autoepistemic logic of first order is a very powerful framework for nonmonotonic reasoning, logic programming, deductive databases, and knowledge representation.This research is partially supported by NSERC grant OGP42193.     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.     

子句型缺省逻辑中的分情形推理

引进一种树型方法以研究缺省逻辑中分情形推理下的Roos扩张,深入讨论了Roos扩张的计算,并分析了Roos扩张与Reiter扩张的关系.为计算Roos扩张,引入了从子句集分解最小文字集的算法.方法对于在缺省逻辑中计算Roos扩张以及分析分情形推理的计算复杂性是有用的.     

DEFAULT LOGICS: A UNIFIED VIEW

Default logic has been introduced for handling reasoning with incomplete knowledge. It has been widely studied, and various definitions have been proposed for it. Most of the variants have been defined by means of fixed points of some operator. We propose here another approach, which is based on a study of the way in which general rules with exceptions, used in a default reasoning process, can contradict one another. We then isolate sets of noncontradicting rules, as large as possible in order to exploit as much information as possible, and construct, for each of these sets of rules, the set of conclusions that can be deduced from it. We show that our framework encompasses most of the existing variants of default logic, allowing those variants to be compared from a knowledge representation point of view. Our approach also enables us to provide an operational definition of extensions in some interesting cases. Proof-theoretical and semantical aspects are investigated.     

Priorities on defaults with prerequisites,and their application in treating specificity in terminological default logic

In a recent paper we have proposed terminological default logic as a formalism that combines means both for structured representation of classes and objects and for default inheritance of properties. The major drawback that terminological default logic inherits from general default logic is that it does not take precedence of more specific defaults over more general ones into account. This behavior has already been criticized in the general context of default logic, but it is all the more problematic in the terminological case where the emphasis lies on the hierarchical organization of concepts.The present paper addresses the problem of modifying terminological default logic such that more specific defaults are preferred. We assume that the specificity ordering is induced by the hierarchical organization of concepts, which means that default information is not taken into account when computing priorities. It turns out that the existing approaches for expressing priorities between defaults do not seem to be appropriate for defaults with prerequisites. Therefore we shall consider an alternative approach for dealing with prioritization in the framework of Reiter's default logic. The formalism is presented in the general setting of default logic where priorities are given by an arbitrary partial ordering on the defaults. We shall exhibit some interesting properties of the new formalism, compare it with existing approaches, and describe an algorithm for computing extensions. In the terminological case, we thus obtain an automated default reasoning procedure that takes specificity into account.This is an extended version of a paper presented at the13th International Joint Conference on Artificial Intelligence, August 1993, Chambery, France.