逻辑程序的Petri网模型

本文探讨了用Ｐｅｔｒｉ网模拟逻辑程序的可能性，给出了用Ｐｅｔｒｉ网来求取逻辑程序的答案的方法，为自动推理提供了依据，并给出了几个实例。     

不确定型逻辑程序的综合

本文提出了基于演绎和知识相结合的通过构造性证明综合程序的方法,进而讨论了不确定型逻辑程序的综合和自动综合的有关问题。用append程序的综合展示了这些方法。     

扩充析取逻辑程序的争论语义

该文探讨争论推理在扩充逻辑程序中的实现及其关系问题.基于“相干原理”,建立了扩充逻辑程序的争论推理框架,多种争论推理形式都可以嵌入其中.特别是提出了一种谨慎语义Acc.同时又定义了良基语义的一种合理扩充Mod,以处理较为大胆的推理形式.另外也研究了相关的理论性质.     

概率逻辑含多重原子交集分解模型的可靠性

文中给出含多重原子公式交集的概率逻辑分解计算模型的可靠性证明。若概率逻辑公式集可被划分为含多重原子交集的子集，且交集的原子个数较少，则分解算法将明显地缩小概率逻辑自动推理线性规划模型的规模。     

基于扩展描述逻辑和逻辑程序的事件动作形式化表示与推理

\"事件\"是比\"概念\"粒度更大的人类知识表示单元,更接近于人类的认知过程。动作作为事件的一个重要要素,表示事件中对象的状态的变化过程。在状态的变化过程中加入时间信息,将动作表示为对象的状态随时间变化而变化的过程,使得动作描述得更加具体。运用事件中的动作、对象和时间要素构建了一个动作形式化体系,研究了事件知识中确定性动作和不确定性动作的语法表示和语义解释。该形式化体系将扩展的带时间维的描述逻辑T-ALC和逻辑程序设计进行整合,增强了动作的表达能力;在动作的推理中,将确定性动作转化为逻辑程序Datalog规则实现动态推理,将不确定性动作转化为Datalog规则实现不确定推理。最后通过银行服务系统实例对动作的形式化表示和推理进行了验证。     

逻辑程序语言中的集合

本文首先讨论了引入集合的意义，然后建立了一种基于集合基贩归约演算，并且对集合项的存储和含集合符号的逻辑数据语言的计算进行了一些探讨。本文所介绍的方法通过规则编译时对存储衣序集的改写，使得对集合匹配通过一般函数的匹配算法就可以完成，提高了计算的效率。     

Horn逻辑程序和形式文法之间的对应关系

The paper researches Horn logic programs with grammatical view. The correspondence between Horn logic programs and grammars is found. The method by which type-0 grammars generate the least Herbrand models of logic programs is found. The method by which Horn logic programs generate the languages of type-0 grammars is found.The characterization of Horn Logic programs that are semantically equavanent to type-2 grammars and type-3 grammars is found.     

Prolog逻辑程序的测试方法

Prolog逻辑程序设计语言具有不同于传统程序设计语言的特点.本文根据测试Prolog程序的实践,提出了一系列为Prolog程序生成测试用例的准则.按这些准则测试实际程序效果良好.这是在逻辑程序测试新领域迈出的一步.     

Experimenting with prototype system DLVK via different approach

Planning allows one to sequence a series of actions to achieve a certain goal. In this paper, we present a short overview of the Disjunctive logic programming under the answer set semantics Then we use the proposed DLV^K planning system against a well-known blocks world planning domain for fully and partially specified initial state facts and the system performance is checked in the run time CPU seconds while increasing the plan length which are the number of steps presenting the plan quality together with security check feature for each plan to know whether the plan generated is secure. Blocks world planning has been widely investigated by planning researchers, primarily due to its simplicity and also because it captures several of the relevant difficulties that are involved in a typical planning domain. The work presented in this paper contributed mainly as a technique compatible with the extensions and improvements of the existing system rather than as a concrete planning system.     

Learning probabilistic logic models from probabilistic examples

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches—abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.     

A logic programming system for nonmonotonic reasoning

The evolution of logic programming semantics has included the introduction of a new explicit form of negation, beside the older implicit (or default) negation typical of logic programming. The richer language has been shown adequate for a spate of knowledge representation and reasoning forms.The widespread use of such extended programs requires the definition of a correct top-down querying mechanism, much as for Prolog wrt. normal programs. One purpose of this paper is to present and exploit a SLDNF-like derivation procedure, SLX, for programs with explicit negation under well-founded semantics (WFSX) and prove its soundness and completeness. (Its soundness wrt. the answer-sets semantics is also shown.) Our choice ofWFSX as the base semantics is justi-fied by the structural properties it enjoys, which are paramount for top-down query evaluation.Of course, introducing explicit negation requires dealing with contradiction. Consequently, we allow for contradiction to appear, and show moreover how it can be removed by freely changing the truth-values of some subset of a set of predefined revisable literals. To achieve this, we introduce a paraconsistent version ofWFSX, WFSX _p , that allows contradictions and for which our SLX top-down procedure is proven correct as well.This procedure can be used to detect the existence of pairs of complementary literals inWESX _p simply by detecting the violation of integrity rulesf L, -L introduced for eachL in the language of the program. Furthermore, integrity constraints of a more general form are allowed, whose violation can likewise be detected by SLX.Removal of contradiction or integrity violation is accomplished by a variant of the SLX procedure that collects, in a formula, the alternative combinations of revisable literals' truth-values that ensure the said removal. The formulas, after simplification, can then be satisfied by a number of truth-values changes in the revisable, among true, false, and undefined. A notion of minimal change is defined as well that establishes a closeness relation between a program and its revisions. Forthwith, the changes can be enforced by introducing or deleting program rules for the revisable literals.To illustrate the usefulness and originality of our framework, we applied it to obtain a novel logic programming approach, and results, in declarative debugging and model-based diagnosis problems.     

PRL: A probabilistic relational language

In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into a logic programming framework. We show how to represent varying degrees of complexity in the semantics including attribute uncertainty, structural uncertainty and identity uncertainty. Our approach is similar in spirit to the work in Bayesian Logic Programs (BLPs), and Logical Bayesian Networks (LBNs). However, surprisingly, there are still some important differences in the resulting formalism; for example, we introduce a general notion of aggregates based on the PRM approaches. One of our contributions is that we show how to support richer forms of structural uncertainty in a probabilistic logical language than have been previously described. Our goal in this work is to present a unifying framework that supports all of the types of relational uncertainty yet is based on logic programming formalisms. We also believe that it facilitates understanding the relationship between the frame-based approaches and alternate logic programming approaches, and allows greater transfer of ideas between them. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

A characterization of answer sets for logic programs

Checking if a program has an answer set, and if so, compute its answer sets are just some of the important problems in answer set logic programming. Solving these problems using Gelfond and Lifschitz's original definition of answer sets is not an easy task. Alternative characterizations of answer sets for nested logic pro- grams by Erdem and Lifschitz, Lee and Lifschitz, and You et al. are based on the completion semantics and various notions of tightness. However, the notion of tightness is a local notion in the sense that for different answer sets there are, in general, different level mappings capturing their tightness. This makes it hard to be used in the design of algorithms for computing answer sets. This paper proposes a characterization of answer sets based on sets of generating rules. From this char- acterization new algorithms are derived for computing answer sets and for per- forming some other reasoning tasks. As an application of the characterization a sufficient and necessary condition for the equivalence between answer set seman- tics and completion semantics has been proven, and a basic theorem is shown on computing answer sets for nested logic programs based on an extended notion of loop formulas. These results on tightness and loop formulas are more general than that in You and Lin's work.     

一种基于ILP和ASP的学习B语言描述的动作模型方法

动作模型学习可以使Agent主动适应动态环境中的变化,从而提高Agent的自治性,同时也可为动态域建模提供一个初步模型,为后期的模型完善和修改提供了基础.通过结合归纳逻辑程序设计(Inductive Logic Program-ming,ILP)和回答集程序设计(Answer Set Programming,ASP),设计了一个学习B语言描述的动作模型算法,该算法可以在混合规模的动态域中进行学习,并采用经典规划实例验证了该学习算法的有效性.