知识处理中的推理机设计

本文重点研究了ＳＫＹ＾〔１〕知识处理中推理机的实现技术、推理解释算法和运行栈、中间代码段等重要资源的管理技术。ＳＫＹ拥有三部推理机：对象推理机、规则集推理机和方法推理机。对象推理机主要完成槽的检索定位任务它还具有控制和协调其它推理机的作用;规则集推理机采用正向推理策略完成对规则集的推理操作;方法推理机主要完成解释执行方法体的任务。     

基于可能性理论的聚类分析

可能性理论是处理不确定、不精确数据或信息的一种方法,它是建立在模糊集理论上,在聚类中引入可能性理论,符合人们的思维方式。该文根据可能性理论,从聚类集合的对象之间的相似度上考虑提出了可能性匹配,它通过可能相似程度和必要相似程度概念来表达,可能相似程度尽可能地消除人聚类的主观误差,而必要相似程度尽可能保存信息,符合人们的思维方式,在聚类分析广泛应用的现实中具有较好的应用前景。     

一种基于语义Tableau的不相容知识处理方法

Tableau作为自动推理的有效方法之一,在许多人工智能领域中有重要的应用。在Tableau基础上,该文提出了Tableau开放和封闭的推理标准,应用于数据库实例不满足完整性约束的不相容关系数据库中,并对其进行修正。采用了逻辑程序的方法,对数据库进行修正,解决了传统修正方法丢失信息、出现不相容等问题。     

使用背景知识处理不完全信息

1 引言不完全性信息大致可以分为两类:其一表现出不精确性,即属性值的内容不唯一;其二呈现不确定件,即属性值的真值程度不肯定。这两种情形下许多问题的通用求解往往是NP问题,实际中只能在特定的约束和限制下解决。尽管人们很少意识到环境映射知识的存在,然而它却在人类认知和智能活动中扮演了十分重要的角色。人们在遭遇一个相对陌生的环境时,总是试图找出一个与之相类似的熟悉场景作参考,继而获得一个加之于原问题之上的弱结构关系(不一定完全正确),使不完全性信息下的问题求解摆脱搜索空间过大、知识表达不清晰等的困扰。本文给出一种通过相似环境映射获得欲处理问题背景知识的方法,并用结构关系R显式表示。具体用不精确信息的处理过程例示说明之。     

表示和处理缺省知识的多维模糊推理方法及算法

本文采用MAX－○复合运算,在基于模糊关系Rb的多维模糊推理Sugeno-Takagi的推理框架中,利用可能性量化语句,从理论上提出了一种表示和处理和缺省知识的多维模糊推理方法,从而提高模糊推理表示和处理知识的能力。在此理论基础上,依据J.F.Baldwin真值限定的概念,提出了一种表示和处理缺省知识的多媒模糊真值推理算法。     

可能性传播图模型的专家知识获取方法

1.引言人工智能的核心问题之一是如何表达己有知识以及如何应用已有知识进行分析处理或推理,以得到新的知识。其中,尤以不确定性知识表达和推理最为重要,也十分困难。但由于它很有现实意义,目前是国际上研究的热点。不确定的知识表达可分为两大类:一类是基于概率的方法,包括信度网、马尔可夫网以及PROSPECTOR中使用的方法等。一类是非概率的方法,包括MYCIN的信度因子、模糊逻辑以及Dempster-Shafer的证据理论等。非概率的方法虽然在各自的应用领域都取得了一定成果,但在运用过程中人们越来越意识到这类方法的不足。目前,以信度网为代表的概率方法己成为不确定性知识表达的主流方法。信度网又名贝叶斯网络,是一个有向无环的图形结构。它具有理论上的严格性和一致性,以及有效的局部计算机制和直观的图形化知识表达。然而,信度网络也存在许多不足:如处理多连通问题和因果循环问题的     

基于可能性理论的案例决策方法研究

提出基于可能性理论与基于案仍推理相结合的双层CBDT决策方法。第1层：决策者应用基于案例推理方法,通过建立在连续集合上问题的可能性分布,结合不同的决策方法对决策问题的不确定性进行定性分析;第2层：利用期望效用理论在与问题的可能性分布相关联的方案中选择具体方案。该方法可避免以往基于案例决策方法中所蕴含的一些技术难题。     

可能性扩展规则的推理和知识编译

在扩展规则的基础上提出了可能性扩展规则。给出了基于可能性扩展规则的可能性逻辑推理方法,利用互补因子的概念来估价推理问题的复杂度。扩展了经典逻辑的蕴含可控制类和可满足可控制类的定义,提出了可能性蕴含可控制类、不一致性程度计算可控制类的概念。在可能性扩展规则的基础上提出了EPPCCCL(each pair of possibilistic clauses contains complementary literals)理论,并证明了该理论是在最优化形式蕴含可控制类和不一致性程度计算可控制类中的,可以作为可能性     

CONFLUENT POSSIBILITY DISTRIBUTIONS AND THEIR REPRESENTATIONS

Possibilistic distributions admit both measures of uncertainty and (metric) distances defining their information closeness. For general pairs of distributions these measures and metrics were first introduced in the form of integral expressions. Particularly important are pairs of distributions p and q which have consonant ordering—for any two events x and y in the domain of discourse p(x)⪋ p(y) if and only if q(x) ⪋ q(y). We call such distributions confluent and study their information distances.

This paper presents discrete sum form of uncertainty measures of arbitrary distributions, and uses it to obtain similar representations of metrics on the space of confluent distributions. Using these representations, a number of properties like additivity. monotonicity and a form of distributivity are proven. Finally, a branching property is introduced, which will serve (in a separate paper) to characterize axiomatically possibilistic information distances.     

面向篇章理解的分布式知识处理系统

采用分布式知识处理方法求解自然语言篇章理解问题是人工智能领域中重要的研究课题,本文论述一个面向篇章理解的分布式知识处理系统——DKPS/TH-2,首先介绍体系结构,然后详细讨论所采用的问题求解方法、分解分布算法、协作通信策略、知识库及管理机制,最后通过初步试验对系统做出评价。     

一种基于表处理的知识内部表示及Turbo Prolog实现

本文介绍一种基于表处理知识内部表示方式，用这种方式，不仅可以表示基于逻辑真伪的变量，也可以描述量的量化程度，并为此设计了推理机。     

分布式知识处理系统中基于知识的任务规划模型

分布式知识处理系统的求解主要由四步组成:任务规划、任务分布、协作求解,以及结果综合,其中任务的规划是整个求解过程的基础。目前设计的许多系统缺乏任务规划能力,任务的执行序列和执行任务的任务知识源主要由系统开发者指定,所以系统的求解结果带有很大的主观性,效率也难以提高,本文首先分析了分布式知识处理系统中任务、任务知识源和任务规划;其次采用逻辑-对象知识模型,来表示任务规划问题;最后提出了一种基于知识的任务规划模型。     

Belief models: An order-theoretic investigation

I show that there is a common order-theoretic structure underlying many of the models for representing beliefs in the literature. After identifying this structure, and studying it in some detail, I argue that it is useful. On the one hand, it can be used to study the relationships between several models for representing beliefs, and I show in particular that the model based on classical propositional logic can be embedded in that based on the theory of coherent lower previsions. On the other hand, it can be used to generalise the coherentist study of belief dynamics (belief expansion and revision) by using an abstract order-theoretic definition of the belief spaces where the dynamics of expansion and revision take place. Interestingly, many of the existing results for expansion and revision in the context of classical propositional logic can still be proven in this much more abstract setting, and therefore remain valid for many other belief models, such as those based on imprecise probabilities.     

Towards a Possibilistic Logic Handling of Preferences

The classical way of encoding preferences in decision theory is by means of utility or value functions. However agents are not always able to deliver such a function directly. In this paper, we relate three different ways of specifying preferences, namely by means of a set of particular types of constraints on the utility function, by means of an ordered set of prioritized goals expressed by logical propositions, and by means of an ordered set of subsets of possible choices reaching the same level of satisfaction. These different expression modes can be handled in a weighted logical setting, here the one of possibilistic logic. The aggregation of preferences pertaining to different criteria can then be handled by fusing sets of prioritized goals. Apart from a better expressivity, the benefits of a logical representation of preferences are to put them in a suitable format for reasoning purposes, or for modifying them.     

A Formal Model of Multi-Agent Belief-Interaction

A semantics is presented for belief revision in the face of common announcements to a group of agents that have beliefs about each other’s beliefs. The semantics is based on the idea that possible worlds can be viewed as having an internal-structure, representing the belief independent features of the world, and the respective belief states of the agents in a modular fashion. Modularity guarantees that changing one aspect of the world (a belief independent feature or a belief state) has no effect on any other aspect of the world. This allows us to employ an AGM-style selection function to represent revision. The semantics is given a complete axiomatisation (identical to the axiomatisation found by Gerbrandy and Groeneveld for a semantics based on non-wellfounded set theory) for the special case of expansion.     

Automated Refinement of First-Order Horn-Clause Domain Theories

Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. FORTE uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including prepositional theory refinement, first-order induction, and inverse resolution. FORTE is demonstrated in several domains, including logic programming and qualitative modelling.