概率逻辑程序

1 引言最近几十年来,不确定性的管理在知识描述和推理中扮演着越来越重要的角色。为了处理不确定知识,人们提出了各种不同的形式化和方法论,其中大部分是直接或间接地基于概率论的。     

Clausal Logic and Logic Programming in Algebraic Domains

We introduce a domain-theoretic foundation for disjunctive logic programming. This foundation is built on clausal logic, a representation of the Smyth powerdomain of any coherent algebraic dcpo. We establish the completeness of a resolution rule for inference in such a clausal logic; we introduce a natural declarative semantics and a fixed-point semantics for disjunctive logic programs, and prove their equivalence; finally, we apply our results to give both a syntax and semantics for default logic in any coherent algebraic dcpo.     

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。     

Dense Time Logic Programming

In this paper, we describe a dense temporal logic programming (DTLP) framework based on infinite binary trees calledomega trees. We then look at an important subset of omega trees calledordinal treesthat represent only meaningful dense time models. Ordinal trees have the properties ofstabilityandrecurrence, which allow them to be represented finitely. The finite representations calledordinal structurescan be used as temporal data structures and its nodes can be labelled with formulae, giving us the basis for modeling temporally located information. In this paper, we label ordinal structure nodes with Prolog clauses to gettemporal horn clausesthat represent temporal facts, rules and queries. Temporal resolution tries to prove temporal queries from a set of temporal facts and rules using a process calledaligningwhich provides the counterpart of the conventional unification algorithm. Aligning restructures ordinal trees to facilitate the transfer of temporal information between them. We present theoretical results to show that aligning is computable, and that the procedures for aligning and resolution are correct.     

Agent-Oriented Probabilistic Logic Programming

Currently, agent-based computing is an active research area, and great efforts have been made towards the agent-oriented programming both from a theoretical and practical view. However, most of them assume that there is no uncertainty in agents＇ mental state and their environment. In other words, under this assumption agent developers are just allowed to specify how his agent acts when the agent is 100% sure about what is true/false. In this paper, this unrealistic assumption is removed and a new agent-oriented probabilistic logic programming language is proposed, which can deal with uncertain information about the world. The programming language is based on a combination of features of probabilistic logic programming and imperative programming.     

Mathematical Programming Embeddings of Logic

Can theorem proving in mathematical logic be addressed by classical mathematical techniques like the calculus of variations? The answer is surprisingly in the affirmative, and this approach has yielded rich dividends from the dual perspective of better understanding of the mathematical structure of deduction and in improving the efficiency of algorithms for deductive reasoning. Most of these results have been for the case of propositional and probabilistic logics. In the case of predicate logic, there have been successes in adapting mathematical programming schemes to realize new algorithms for theorem proving using partial instantiation techniques. A structural understanding of mathematical programming embeddings of predicate logic would require tools from topology because of the need to deal with infinite-dimensional embeddings. This paper describes the first steps in this direction. General compactness theorems are proved for the embeddings, and some specialized results are obtained in the case of Horn logic.     

The Logic of Computer Programming

Techniques derived from mathematical logic promise to provide an alternative to the conventional methodology for constructing, debugging, and optimizing computer programs. Ultimately, these techniques are intended to lead to the automation of many of the facets of the programming process.     

Incremental search in Constraint Logic Programming

Incremental search consists of adding new constraints or deleting old ones once a solution to a search problem has been found. Although incremental search is of primary importance in application areas such as scheduling, planning, trouble shooting, and interactive problem-solving, it is not presently supported by logic programming languages and little research has been devoted to this topic. This paper presents a scheme to deal efficiently with incremental search problems. The scheme allows the incremental addition and deletion of constraints and is based on re-execution, using parts of computation paths stored during previous computations. The scheme has been implemented as part of the constraint logic programming language CHIP and applied to practical problems. It has shown arbitrarily large (i.e. unbounded) speedups compared with previous approaches on practical problems.     

A Correct Logic Programming Computation of Default Logic Extensions

We present a method of representing some classes of default theories as normal logic programs. The main point is that the standart semantics (i.e., SLDNF-resolution) computes answer substitutions that correspond exactly to the extensions of the represented default theory. This means that we give a correct implementation of default logic. We explain the steps of constructing a logic program LogProg(P, D) from a given default theory (P, D), give some examples, and derive soundness and completeness results.     

约束归纳逻辑程序设计系统

提出了一种新的约束归纳逻辑程序设计方法，并初步实现了一个自顶向下的约束归纳逻辑程序原型系统。该系统能够导出不受变量个数限制的多种形式的线性约束，得出覆盖正例而排斥负例的含约束的Hom子句程序。     

SC-PROLOG解释系统中的约束逻辑程序设计方法

约束逻辑程序设计（ＣＬＰ）方法是提高ＰＲＯＬＯＧ语言效率的一种崭新方法，本文针对ＳＣ┐ＰＲＯＬＯＧ解释系统的实现介绍其相应设计思想，从域变量含义入手，提出了域及约束的存储方法以及约束机制的实现算法，是对逻辑设计方法研究的一点体会     

归纳逻辑程序设计综述

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,是机器学习的前沿研究课题。该文首先从归纳逻辑程序设计的问题背景、类型划分和搜索程序子句三个方面介绍了归纳逻辑程序设计系统的概貌;然后结合实验室的相关研究工作,回顾了归纳逻辑程序设计研究的发展;之后介绍了归纳逻辑程序设计领域中需要深入研究的若干问题,并提出了新的解决思路;最后是总结,以引起读者对归纳逻辑程序设计领域研究的进一步关注。     

约束逻辑程序设计综述

一、引言 约束逻辑程序设计(Constraint Logic Program-ming．CLP)是基于人工智能(AI)中约束满足问题(Constraint Satisfaction Problem．CSP)模型的一种程序设计风范。CLP是逻辑程序设计(LP)的一种推广,是八十年代发展起来的一种新的逻辑程序设计方法。由于它继承了LP简单易懂的说明性描述方法并结合了CSP在求解问题时的效率,使它在解决很多AI问题(如组合问题、资源分配、事务安排等)时有不凡的表现。更由于AI领域中绝大多数问题可以用CLP来表示,所以这一方法已引起了人们的广泛注意,并在八十年代后期得以迅速发展。