双析取逻辑程序设计基础（Ⅱ）

双析取逻辑程序设计是析取逻辑程序设计的一种扩充，其辩论语框架ＢＤＡＳ为逻辑程序设计中的常识推理提供了一种较为合理的语义框架。     

逻辑程序的语义问题(Ⅰ)

说明性语义是逻辑程序研究的重要内容,也是其作为说明性程序的基本特征。近年来由于人们对带否定前提的一般逻辑程序设计的关注,以及逻辑程序与非单调推理的结合,逻辑程序的语义研究出现了许多新结果。本文以早期的Clark语义和最小模型语义为起点,介绍这些新发展的部分内容,主要包括Clark语义的Fitting 3-值扩充,理想模型语义、稳定模型语义、良基模型语义以及它们之间的关系,并在此基研上进一步讨论了说明性语义在逻辑程序设计中的地位和作用。     

逻辑程序的语义问题 I

文明性语义是逻辑程序研究的重要内容，也是其作为说明性程序的基本特征。近年来由于人们对带否定前提了一般逻辑程序设计的关注，以及逻辑程序与非单调推理的结合，逻辑程序的语义研究出现了许多新结果。本文以早期的Ｃｌａｒｋ语义和最小模型语义为起点，介绍这些新发展的部分内容，主要包括Ｃｌａｒｋ语义的Ｆｉｔｔｉｎｇ３－值扩充，理想模型语义，稳定模型语义，良基模型语义以及它们之间的关系，并在此基础上进一步讨论了说明     

POOPL合成逻辑与面向对象的程序设计语言

为了描述知识信息处理系统,人们提出了许多新的程序设计风格,如逻辑程序设计风格、函数程序设计风格、面向规则程序设计风格、面向对象的程序设计风格。但是,没有一种程序设计风格能够完全满足知识信息处理的要求。于是,人们着手探讨合成许多风格的语言。本文论述了合成面向对象与逻辑程序设计两种风格的必要性与可能性以及有关的技术难题,并且简单地介绍了我们研制的合成面向对象与逻辑程序设计两种风格的知识程序设计语言POOPL。它继承了面向对象程序设计对知识库的组织能力和逻辑程序设计对知识的表达能力,并且严格地区别面向对象与逻辑程序设计的语义,以求最大限度地挖掘面向对象程序设计与逻辑程序设计的全部优越性。     

LP在语义Web数据挖掘研究中的应用

语义网络数据挖掘是基于语义网络环境的数据挖掘，它给数据挖掘技术的应用研究提出了新的课题。归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域，它为知识工程等人工智能的应用领域提供了新的强有力的技术支持。分析了现有几种常用数据挖掘技术在语义Web环境下应用的局限性，提出了采用归纳逻辑程序设计（ILP）作为语义Web上适合的数据挖掘技术，给出了应用这种技术的算法描述，通过具体实例验证了其可行性。     

带函数析取逻辑程序的无基集及其应用

基于回答集（也称稳定模型）语义的带函数析取逻辑程序是一种重要的知识表示和推理方法。由于判定一个析取逻辑程序是否有回答集是困难的（Σ2完全的）,目前还没有有效的方法来计算带函数析取逻辑程序的回答集,主要原因之一是检查一个集合是否是回答集是coNP完全的。提出了带函数析取逻辑程序无基集（unfounded sets）的概念,发现了空无基集（unfounded-free sets）与稳定模型之间的一一对应关系,在此基础上,证明了一个逻辑程序的模型是该程序的稳定模型当且仅当它们对应的一个命题公式是不可满足的,从而在理论上为计算带函数析取逻辑程序的回答集提供了一种有效的途径。     

逻辑程序设计的研究

本文包括:逻辑程序设计的发展历史,逻辑程序设计与Horn子句、Prolog语言,逻辑程序设计的特点,逻辑程序的并行性以及逻辑程序设计存在的问题。     

基于稳定模型的软件多样性与安全初探

该文简要阐述了软件多样性与安全性的关系后,基于STABLEMODEL是逻辑程序的语义模型的观点,从软件与逻辑程序设计的关联出发,提出了用逻辑程序设计实现软件多样性的方法。论文首先介绍了逻辑程序中稳定模型的形成、定义、演算方法,通过逻辑程序与稳定模型之间存在的多对一的映射关系,产生软件的多样性。最后,通过具体的分析,提出了基于稳定模型的程序多样性演化方法,这一方法实现了从一个源程序到一系列等价程序的多样性演化,从而提高了系统的鲁棒性和安全性。     

逻辑程序中的对称结构

逻辑程序设计是程序设计的重要方法之一，所谓程序语义，就是赋予程序以意义。语义研究是逻辑程序设计中的一个重要课题，衡量一个给定逻辑程序语义成功与否一般有三个标准：第一，是否与常识相吻合；第二，表达能力如何；第三，是否具有可计算性。用这三个标准衡量，几乎所有已知的逻辑程序语义都有缺点。例如封闭世界假设CWA，与常识相吻合并且计算复杂性不大，但表达能力太弱，只适用于确定的程序。虽然被加以改进，表达能力增强，但计算复杂度增大并且时常与常识相悖。又比如，SLD-归结，SLDNF-归结以及循环不动点语义只对确定的，层次的和分层的程序有效；完全化过程对于非分层的程序还可能导致矛盾。因而，从表达能力上看，这些语义都是受限的。     

逻辑语言剪枝算子的过程语义及其实现

论述了逻辑程序设计中剪枝算子的作用及传统剪枝算子的过程性语义和说明性语义不一致问题;介绍了新型逻辑程序语言〔淑划中的COTRTT11t剪枝算子;通过引入一组定义描述其过程语义,并进一步阐述了剪枝算子和延迟计算规则之间的关系,讨论了Godel语言的剪枝策略及控制机制,从而为逻辑程序语言的实现提供了依据。     

The Least Fixpoint Transformation for Disjunctive Logic Programs

The paradigm of disjunctive logic programming(DLP)enhances greatly the expressive power of normal logic programming(NLP)and many(declarative)semantics have been defined for DLP to cope with various problems of knowledge representation in artificial intelligence.However,the expressive ability of the semantics and the soundness of program transformations for DLP have been rarely explored.This paper defines an immediate consequence operatro T^GP for each disjunctive program and shows that T^GP has the least and computable fixpoint Lft(P),Lft is,in fact,a program transformation for DLP,which transforms all disjunctive programs into negative programs.It is shown that Lft preserves many key semantics,including the disjunctive stable models,well-founded model,disjunctive argunent semantics DAS,three-valued models,ect.Thic means that every disjunctive program P has a unique canonical form Lft(P)with respect to these semantics.As a result,the work in this paper provides a unifying framework for studying the expressive ability of various semantics for DLP On the other hand,the computing of the above semantics for negative programs is ust a trivial task,therefore,Lft(P)is also an optimization method for DLP.Another application of Lft is to derive some interesting semantic results for DLP.     

Semantics and complexity of recursive aggregates in answer set programming

The addition of aggregates has been one of the most relevant enhancements to the language of answer set programming (ASP). They strengthen the modelling power of ASP in terms of natural and concise problem representations. Previous semantic definitions typically agree in the case of non-recursive aggregates, but the picture is less clear for aggregates involved in recursion. Some proposals explicitly avoid recursive aggregates, most others differ, and many of them do not satisfy desirable criteria, such as minimality or coincidence with answer sets in the aggregate-free case.In this paper we define a semantics for programs with arbitrary aggregates (including monotone, antimonotone, and nonmonotone aggregates) in the full ASP language allowing also for disjunction in the head (disjunctive logic programming — DLP). This semantics is a genuine generalization of the answer set semantics for DLP, it is defined by a natural variant of the Gelfond–Lifschitz transformation, and treats aggregate and non-aggregate literals in a uniform way. This novel transformation is interesting per se also in the aggregate-free case, since it is simpler than the original transformation and does not need to differentiate between positive and negative literals. We prove that our semantics guarantees the minimality (and therefore the incomparability) of answer sets, and we demonstrate that it coincides with the standard answer set semantics on aggregate-free programs.Moreover, we carry out an in-depth study of the computational complexity of the language. The analysis pays particular attention to the impact of syntactical restrictions on programs in the form of limited use of aggregates, disjunction, and negation. While the addition of aggregates does not affect the complexity of the full DLP language, it turns out that their presence does increase the complexity of normal (i.e., non-disjunctive) ASP programs up to the second level of the polynomial hierarchy. However, we show that there are large classes of aggregates the addition of which does not cause any complexity gap even for normal programs, including the fragment allowing for arbitrary monotone, arbitrary antimonotone, and stratified (i.e., non-recursive) nonmonotone aggregates. The analysis provides some useful indications on the possibility to implement aggregates in existing reasoning engines.     

A new approach to hybrid probabilistic logic programs

This paper presents a novel revision of the framework of Hybrid Probabilistic Logic Programming, along with a complete semantics characterization, to enable the encoding of and reasoning about real-world applications. The language of Hybrid Probabilistic Logic Programs framework is extended to allow the use of non-monotonic negation, and two alternative semantical characterizations are defined: stable probabilistic model semantics and probabilistic well-founded semantics. These semantics generalize the stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of Hybrid Probabilistic Logic programs for programs without negation. It is the first time that two different semantics for Hybrid Probabilistic Programs with non-monotonic negation as well as their relationships are described. This proposal provides the foundational grounds for developing computational methods for implementing the proposed semantics. Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning. An erratum to this article can be found at     

A logical foundation for the semantic Web

World Wide Web (WWW) has been one of important channels from which people acquire information and services, but most web pages are only used by humans at pre-sent, and these pages cannot be processed and understood automatically by computers. The semantic Web is an essential reformation of Web. The main objective of the seman-tic Web is to enrich Web with semantics and make Web be understood by computers, in order to communicate and cooperate between people and computer. The key of the se-…     

N-SHOQ(D):描述逻辑SHOQ(D)的一个非单调扩展

描述逻辑SHOQ(D)给出了Web本体语言DAML OIL的语义,但SHOQ(D)只能处理严格成立的完备知识,不能处理在实际情况中经常出现的不完备知识．对描述逻辑SHOQ(D)进行扩展,提出了能够处理不完备知识的非单调描述逻辑N—SHOQ(D)．给出了N—SHOQ(D)的语法和语义,定义了N—SHOQ(D)中的蕴涵推理关系,研究了N—SHOQ(D)所具有的性质．N—SHOQ(D)为扩展DAML OIL语言到能够处理不完备知识的情形提供了语义支持．     

一种新的辩论推理模式及其应用

文中定义了一个新的辩论推理模式,建立了一个形式化的知识表示框架,并把它应用于研究扩展逻辑程序类的说明语义,结果表明,新语义克服了择优语义的不足。作者还根据上述研究结果实现了逻辑程序设计风格下的知识框架。     

Semantic forgetting in answer set programming

The notion of forgetting, also known as variable elimination, has been investigated extensively in the context of classical logic, but less so in (nonmonotonic) logic programming and nonmonotonic reasoning. The few approaches that exist are based on syntactic modifications of a program at hand. In this paper, we establish a declarative theory of forgetting for disjunctive logic programs under answer set semantics that is fully based on semantic grounds. The suitability of this theory is justified by a number of desirable properties. In particular, one of our results shows that our notion of forgetting can be entirely captured by classical forgetting. We present several algorithms for computing a representation of the result of forgetting, and provide a characterization of the computational complexity of reasoning from a logic program under forgetting. As applications of our approach, we present a fairly general framework for resolving conflicts in inconsistent knowledge bases that are represented by disjunctive logic programs, and we show how the semantics of inheritance logic programs and update logic programs from the literature can be characterized through forgetting. The basic idea of the conflict resolution framework is to weaken the preferences of each agent by forgetting certain knowledge that causes inconsistency. In particular, we show how to use the notion of forgetting to provide an elegant solution for preference elicitation in disjunctive logic programming.     

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 framework for linguistic logic programming

Lawry's label semantics for modeling and computing with linguistic information in natural language provides a clear interpretation of linguistic expressions and thus a transparent model for real‐world applications. Meanwhile, annotated logic programs (ALPs) and its fuzzy extension AFLPs have been developed as an extension of classical logic programs offering a powerful computational framework for handling uncertain and imprecise data within logic programs. This paper proposes annotated linguistic logic programs (ALLPs) that embed Lawry's label semantics into the ALP/AFLP syntax, providing a linguistic logic programming formalism for development of automated reasoning systems involving soft data as vague and imprecise concepts occurring frequently in natural language. The syntax of ALLPs is introduced, and their declarative semantics is studied. The ALLP SLD‐style proof procedure is then defined and proved to be sound and complete with respect to the declarative semantics of ALLPs. © 2010 Wiley Periodicals, Inc.