概率逻辑、不确定逻辑和模糊逻辑之比较

通过一个实例分析比较了概率逻辑、主观概率逻辑、不确定逻辑和模糊逻辑的思想方法。提出了自己的观点：基于数据统计的概率逻辑是最科学的。不确定逻辑比主观概率逻辑更科学。当具有不确定性的原子命题具有独立性时,不确定逻辑和模糊逻辑的观点是一致的。而对于处理带有不确定性的相关性命题,不确定逻辑比模糊逻辑更科学。但是模糊逻辑在建立推理理论方面见长。     

USING META-PROGRAMMING,CONSTRAINT LOGIC PROGRAMMING AND APPROXIMATE REASONING FOR EXPERT SYSTEMS DESIGN

Abstract

This paper presents an enhancement of the CARESS system—A Constraint Approximative Reasoning System Support—introduced in (Popescu and Roventa, 1994). CARESS is an experimental system with primarily two objectives:

(1)knowledge representation and manipulation techniques and to implement them in PROLOG III, and

(2) to develop a knowledge programming environment for building expert systems. We discuss here the use of meta-programming, constraint logic programming and approximate reasoning for the design of expert systems

It has already been proven that meta-programming and logic programming are powerful techniques for expert system design. Fuzzy logic can be used to model one kind of uncertainty. Constraint logic programming is useful for dealing with the constraints given by operations using fuzzy sets.     

Uncertainty measures for rough formulae in rough logic: An axiomatic approach

Rough set theory, initiated by Pawlak, is a mathematical tool in dealing with inexact and incomplete information. Various types of uncertainty measure such as accuracy measure, roughness measure, etc, which aim to quantify the imprecision of a rough set caused by its boundary region, have been extensively studied in the existing literatures. However, a few of these uncertainty measures are explored from the viewpoint of formal rough set theory, which, however, help to develop a kind of graded reasoning model in the framework of rough logic. To solve such a problem, a framework of uncertainty measure for formulae in rough logic is presented in this paper. Unlike the existing literatures, we adopt an axiomatic approach to study the uncertainty measure in rough logic, concretely, we define the notion of rough truth degree by some axioms, such a notion is demonstrated to be adequate for measuring the extent to which any formula is roughly true. Then based on this fundamental notion, the notions of rough accuracy degree, roughness degree for any formula, and rough inclusion degree, rough similarity degree between any two formulae are also proposed. In addition, their properties are investigated in detail. These obtained results will be used to develop an approximate reasoning model in the framework of rough logic from the axiomatic viewpoint.     

因果知识的表示和推理

本文介绍了因果推理的两个主要应用：预测行为的间接结果，找出给定事实的真正原因，指出了用逻辑描述的因果关系在解决这两个问题中存在的不足。本文也简单地介绍了J．Pearl提出的因果推理方法，该方法的基本思想是把因果推理看成是一种“计算模式”，而这种把智能归结为“计算模式”的想法正是认知科学中的核心思想。本文最后比较了基于逻辑的方法和基于认知科学的方法实现智能的区别，指出了因果推理应该在认知科学的框架内得到解决。     

A rough set approach to reasoning under uncertainty

Abstract

Reasoning with uncertain information is a problem of key importance when dealing with information about the real world. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem. The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. This paper presents an introduction to various forms of reasoning under uncertainty that are based on rough sets. In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion of possible worlds. Methods of combining the truth values are developed so that they may be propagated when augmented logic formulae are combined, and their use is demonstrated in theorem proving.     

A first-order logic for reasoning under uncertainty using rough sets

Reasoning with uncertain information is a problem of key importance when dealing with knowledge from real situations. Obtaining the precise numbers required by many uncertainty-handling formalisms can be a problem when building real systems. The theory of rough sets allows us to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. The authors develop a set of symbolic truth values based upon rough sets which may be used to augment predicate logic, and provide methods for combining these truth values so that they may be propagated when augmented logic formulae are used in automated reasoning.     

Paraconsistent Declarative Semantics for Extended Logic Programs

We introduce a fixpoint semantics for logic programs with two kinds of negation: an explicit negation and a negation-by-failure. The programs may also be prioritized, that is, their clauses may be arranged in a partial order that reflects preferences among the corresponding rules. This yields a robust framework for representing knowledge in logic programs with a considerable expressive power. The declarative semantics for such programs is particularly suitable for reasoning with uncertainty, in the sense that it pinpoints the incomplete and inconsistent parts of the data, and regards the remaining information as classically consistent. As such, this semantics allows to draw conclusions in a non-trivial way, even in cases that the logic programs under consideration are not consistent. Finally, we show that this formalism may be regarded as a simple and flexible process for belief revision.     

复杂控制系统随机参量的广义自相关性研究

研究泛逻辑推理中广义自相关系数k值的求解方法,提出了基于泛逻辑的复杂控制系统不确定性推理模型,给出了求解k值的一般步骤和相关定理.研究了3种连续型随机参量的广义自相关性:均匀分布的k值恒等于0.5,是一种理想的精确估计模型;没有基变换时的指数分布和标准正态分布的k值均小于0.5,它们对随机参量的逻辑值呈偏小估计.推导出了求解N范数和k值的通用公式,建立起泛逻辑理论和不确定性推理与控制之间的实用性桥梁.     

A probabilistic model-based method for diagnosis

Two paradigms for reasoning under uncertainty have evolved from research into artificial intelligence, namely the qualitative and the quantitative ones. Non-numerical theories which typify the first paradigm exhibit inherent limitations. On the other hand, most of the quantitative reasoning approaches lack the salient properties of completeness and consistency because they do not comply with the requirements of a proven theory such as probability theory or mathematical logic. This failure to comply with a formal method has often arisen out of a necessity to handle uncertainty in an already existing reasoning mechanism.Our model is built using strict probability theory to handle the uncertainty. Probability theory is also used to provide the reasoning mechanism. This ensures consistency and completeness in the model. Syntactic and semantic rules are defined for updating prior with posterior knowledge in a diagnostic task and an inference scheme is developed. An unexpected by-product of this inference scheme is that, whilst no attempt was made to emulate human thought, the model captures qualitative relationships which some consider to be present in human reasoning.     

一种基于产生式规则的不确定推理模板模型的研究

该文针对现有不确定推理模型,结合专家知识不确定性在产生式规则系统中的体现,归纳出一种更接近人类专家处理不确定性和方便人们理解与构造实例模型的不确定推理模板模型。该模板模型可作为现有模型的分析模板和新的基于产生式规则的不确定推理模型研究与构造的基本框架模板。该文详细阐述了该模板模型的组成和工作原理,并用它对现有不确定推理模型进行了实例分析;最后,指出该模板模型各组成子模型的研究方向。     

关于CTL与E_AG_(LE)两种规划扩展目标表示语言的语义比较

在不确定的智能规划领域中,CTL和EAGLE是两种重要的扩展目标表示语言.虽然与CTL相比EAGLE具有可以表示规划意图和失败处理机制的特点,但是有关严格比较这两种目标表示语言语义的研究工作还不多.文章在规划的执行结构这一语义层次上对这两种语占做了严格的比较,证明了对于许多包括原来曾被认为无法用CTL表示的EAGLE规划目标而言,都存在着一个与之语义等价的CTL规划目标,并且进一步分析了这两种语言在表示规划目标和指导规划求解这两个层次上的优缺点.     

Hypothetical reasoning in logic programs

In order to express incomplete knowledge, extended logic programs have been proposed as logic programs with classical negation along with negation as failure. This paper discusses ways to deal with a broad class of common sense knowledge by using extended logic programs. For this purpose, we present a uniform approach for dealing with both incomplete and contradictory programs, as a simple framework of hypothetical reasoning in which some rules are dealt with as candidate hypotheses that can be used to augment the background theory. This theory formation framework can be used for default reasoning, contradiction removals, the closed world assumption, and abduction. We also show a translation of the theory formation framework to an extended logic program whose answer sets correspond to the consistent belief sets of augmented theories.     

Knowledge representation in fuzzy logic

The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control     

A target identification comparison of Bayesian and Dempster-Shafermultisensor fusion

This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory     

自然语言中时间信息的模型化

在自然语言理解中，时间一个重要的语境因素，本文提出一种独立于句子表层形式的多层次时间语义结构，浅层语义结构是时间描述的最化语义表示，深层语义结构描述事件的动态属性和存在特征，该时间语义模型表示时刻和时段，将时间基点明确区分为物理时间基点和说话者时间基点，并提供通用的时间语义计算方法，将各语言的时间描述映射到时间轴上，在该模型基础上，从语义观点出发，建立时，体的可计算模型及各事件时间相关性计算模型。     

Hybrid knowledge bases

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics     

Encompassing full nonmonotonicity within belief modification activity

 In this paper, starting from an analysis of the general activity of belief modification, the concept of full nonmonotonicity is introduced and discussed. Such concept is based on the identification of two different types of nonmonotonicity and includes all the cases of belief modification that may occur in reasoning activity. This distinction cannot be encompassed by the most known nonmonotonic logic formalisms nor by the classical view of conditioning and belief revision axiomatically set up by Gardenfors [9]. A conceptual model able to correctly capture fully nonmonotonic reasoning is discussed: such model includes an explicit representation for the concepts of uncertainty about the applicability of a piece of knowledge and of reasoning attitude. Finally, the issue of formalizing fully nonmonotonic reasoning is discussed and preliminary formalization proposals are introduced.     

Fuzzy temporal reasoning for process supervision

Abstract: Process supervision consists of following the temporal evolution (change) of process behaviours. This task has usually been performed based on the knowledge and experience of domain experts and operators. Actually, these experts and operators almost always express their experience and knowledge about process evolution in an imprecise, fuzzy and vague way. A good supervision system should be capable of dealing at once with two different kinds of knowledge: time and uncertainty.
For many years, time and uncertainty have been two of the most important topics in Artificial Intelligence research and applications. Many approaches have been proposed to deal with either one or the other. Among the various approaches for time, reified logic has been considered as the most influent one. Possibilistic logic, on the other hand, has shown its ability to handle uncertain knowledge and information. This paper describes an approach for managing temporal uncertainty based on fuzzy logic and possibility theory. A fuzzy temporal expert system shell has been developed to perform process supervision tasks.     

基于概念格的语言真值不确定性推理*

利用概念格来实现不确定性推理的过程中,给出了一个具体的语言真值格蕴涵代数的完备结构;作为概念格的扩充理论,提出了用于处理不确定性信息的语言真值概念格,并基于语言真值概念格给出了内逼近不确定性推理规则和外逼近不确定性推理规则,进而验证了这两种规则的还原性。     

Reasoning with Sets of Defaults in Default Logic

We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory , consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension.