Qualitative reasoning with imprecise probabilities

This paper investigates the possibility of performing automated reasoning in probabilistic knowledge bases when probabilities are expressed by means of linguistic quantifiers. Data are expressed in terms of ill-known conditional probabilities represented by linguistic terms. Each linguistic term is expressed as a prescribed interval of proportions. Then instead of propagating numbers, qualitative terms are propagated in accordance with the numerical interpretation of these terms. The quantified syllogism, modeling the chaining of probabilistic rules, is studied in this context. It is shown that a qualitative counterpart of this syllogism makes sense and is fairly independent of the thresholds defining the linguistically meaningful intervals, provided that these threshold values remain in accordance with the intuition. The inference power is less than a full-fledged probabilistic constraint propagation device but corresponds better to what could be thought of as commonsense probabilistic reasoning. Suggestions that may improve the inferencing power in the qualitative setting are proposed.This paper is an extended and revised version of a paper entitled A Symbolic Approach to Reasoning with Linguistic Quantifiers inProc. 8th Conf. Uncertainty in Artificial Intelligence Dubois, D., Wellman, M.P., D'Ambrosio, B., and Smets, Ph. (Eds.), Morgan Kaufmann, pp. 74–82, 1992.     

Signed Systems for Paraconsistent Reasoning

We present a novel approach to paraconsistent reasoning, that is, to reasoning from inconsistent information. The basic idea is the following. We transform an inconsistent theory into a consistent one by renaming all literals occurring in the theory. Then, we restore some of the original contents of the theory by introducing progressively formal equivalences linking the original literals to their renamings. This is done as long as consistency is preserved. The restoration of the original contents of the theory is done by appeal to default logic. The overall approach provides us with a family of paraconsistent consequence relations.Our approach is semantical because it works at the level of the propositions; it deals with the semantical link between a proposition and its negation. The approach is therefore independent of the combination of the connectives that are actually applied to the propositions in order to form entire formulas.     

关于用VB实现知识推理的研究

专家系统核心部分也是难点部分是知识的表示与推理,为了更好地实现专家系统的诊断功能,在典型的故障诊断的各种征兆已广泛用于故障的判断情况下,利用VB和Access数据库,实现了故障的知识推理,可视性强。为故障预报和故障诊断模块提供了分析基础。     

Representing and Reasoning about Motion in a Two-Dimensional World

We present a point-based spatiotemporal first-order logic for representing the qualitative and quantitative spatial temporal knowledge needed to reason about motion in a two-dimensional space. A feature of the logic is the uniform treatment of space and time. The knowledge of a simplified world, a two-dimensional street network with active traffic lights, is represented, and the reasoning problem of how a robot moves from one place to another in the world is formalized with the proposed logic.     

一种基于规则的神经网络的知识表示和推理方法

在医学专家系统中，如何根据各种疾病特征，推断出患某种疾病，这是一个比较复杂的问题，本文给出了一种基于规则的人工神经网络的知识表示和知识推理方法，从规则集中自动构造网络的结构、确定隐层节点数和连接权值，用并行的方法进行推理，最后，给出了泌尿外科常见疾病诊断的一个实例应用。     

A Classification and Survey of Preference Handling Approaches in Nonmonotonic Reasoning

In recent years, there has been a large amount of disparate work concerning the representation and reasoning with qualitative preferential information by means of approaches to nonmonotonic reasoning. Given the variety of underlying systems, assumptions, motivations, and intuitions, it is difficult to compare or relate one approach with another. Here, we present an overview and classification for approaches to dealing with preference. A set of criteria for classifying approaches is given, followed by a set of desiderata that an approach might be expected to satisfy. A comprehensive set of approaches is subsequently given and classified with respect to these sets of underlying principles.     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

Petri网的正向推理算法

提出了一种建立在petri网的基本结构上的形式化正向推理算法,通过建立petri网的关联矩阵、标识向量和激发向量,将petri网与矩阵运算结合,可以在petri网模型中抽取一个子模型,从而把一个大的、复杂的系统转化为一个只与问题相关的小的系统来处理。该算法充分利用了petri网的并行处理能力,缩小了后续应用的范围,加速了后续应用的效率。     

Informational Logic as a Tool for Automated Reasoning

A logical entropy-based Informational Logic is presented which provides new tools for probabilistic automated reasoning and knowledge representation. Applications in automated theorem proving are examined, and a decision theory for probabilistic theorems is proposed.     

The Logical Approach to Temporal Reasoning

Temporal reasoning started to be considered as a subject of study in artificial intelligence in the late 1970's. Since then several ways to represent and use temporal knowledge have been suggested. As a result of that, there are several formalisms capable of coping with temporal notions in some way or other. They range from isolated proposals to complex systems where the temporal aspect is used together with other important features for the task of modelling an intelligent agent. The purposes of this article are to summarize logic-based temporal reasoning research and give a glance on the different research tracks envisaging future lines of research. It is intended to be useful to those who need to be involved in systems having these characteristics and also an occasion to present newcomers some problems in the area that still waits for a solution.     

概念知识表示和推理

Nilsson教授首先提出了代数格应用于概念知识表示的思想．其优点在于知识表示的代数特性和图示特性，在此基础上．表文把代数格与PROLOG相结合．定义了一种基于概念的逻辑编程语言．其语言具有比PROLOG更抽象、更方便的编程风格．此外．给出了该逻辑编程语言的匹配算法．     

基于推理的作战方案评估系统研究

根据作战方案评估要素对评估系统的要求进行了系统功能需求分析,根据该分析设计了作战方案评估系统的总体架构,采用面向对象知识表示法构建了评估系统的知识库,设计了基于规则推理方法的系统推理机,并给出了解释子系统的解释机制。该系统能够利用军事专家的经验和军事决策规则对作战方案进行评估,降低了人的主观因素对评估结果的影响,同时提高了评估结论的可信度。     

属性Petri网模型及其推理算法

定性映射是从哲学上事物的质-量互变规律出发,以感觉特征抽取为依托而建立的脑思维数学模型。文章以定性映射基本定义为基础讨论了属性Petri网的基本模型,给出了其形式定义和基本运行机制,描述了基于属性Petri网的知识表示模式,最后给出了属性Petri网的相应推理算法。该模型在以定性判断为特点的诊断系统中能得到较好地应用。     

A Default Logic Based Framework for Context-Dependent Reasoning with Lexical Knowledge

Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.     

Topological Spatio–Temporal Reasoning and Representation

We present here a theory of motion from a topological point of view, in a symbolic perspective. Taking space–time histories of objects as primitive entities, we introduce temporal and topological relations on the thus defined space–time to characterize classes of spatial changes. The theory thus accounts for qualitative spatial information, dealing with underspecified, symbolic information when accurate data are not available or unnecessary. We show that these structures give a basis for commonsense spatio–temporal reasoning by presenting a number of significant deductions in the theory. This can serve as a formal basis for languages describing motion events in a qualitative way.     

基于粗糙集方法的不完备信息系统的知识推理

含空值决策表的分析一直是智能信息处理领域的一个难点,论文讨论基于赋值容差关系的数据不完全的信息系统中的数据处理方法,并用实例说明不完全决策表中属性约简和决策规则的发现方法。     

Efficient Processing of Queries and Assertions about Qualitative and Quantitative Temporal Constraints

A critical problem for managers of temporal information is the treatment of assertions and of complex types of queries because in many cases the treatment could involve reasoning on the whole knowledge base of temporal constraints. We propose an efficient approach to this problem. First, we show how different types of queries can be answered (in a complete way) in a time polynomial in the dimension of the query and independently of the dimension of the knowledge base. Second, we provide an efficient (and complete) procedure to deal with sessions of interleaved assertions and queries to the knowledge base. We provide both analytical and experimental evaluations of our approach, and we discuss some application areas.