Reasoning in Medical and Tutoring Systems: A Decidable First-Order Temporal Paraconsistent Non-Commutative Logic

Reasoning in medical and tutoring systems requires expressions relating not only to time-dependency, paraconsistency, constructiveness, and resource-sensitivity, but also order-sensitivity. Our objective in this study is to construct a decidable rst-order logic for appropriately expressing this reasoning. To meet this objective, we introduce a rst-order temporal paraconsistent non-commutative logic as a Gentzen-type sequent calculus. This logic has no structural rules but has some bounded temporal operators and a paraconsistent negation connective. The main result of this study is to show this logic to be decidable. Based on this logic, we present some illustrative examples for reasoning in medical and tutoring systems.     

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。     

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.     

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.     

Default Reasoning and Generics

There are numerous logical formalisms capable of drawing conclusions using default rules. Such systems, however, do not normally determine where the default rules come from; i.e., what it is that makes Birds fly a good rule, but Birds drive trucks a bad one.
Generic sentences such as Birds fly are often used informally to describe default rules. I propose to take this characterization seriously, and claim that a default rule is adequate if the corresponding generic sentence is true. Thus, if we know that Tweety is a bird, we may conclude by default that Tweety flies, just in case Birds fly is a true sentence.
In this paper, a quantificational account of the semantics of generic sentences is presented. It is argued that a generic sentence is evaluated not in isolation, but with respect to a set of relevant alternatives. For example, Mammals bear live young is true because among mammals that bear live young, lay eggs, undergo mitosis, or engage in some alternative form of procreation, the majority bear live young. Since male mammals do not procreate in any form, they do not count. Some properties of alternatives are presented, and their interactions with the phenomena of focus and presupposition is investigated.
It is shown how this account of generics can be used to characterize adequate default reasoning systems, and several desirable properties of such systems are proved. The problems of the automatic acquisition of rules from natural language are discussed. Because rules are often explicitly expressed as generics, it is argued that the interpretation of generic sentences plays a crucial role in this endeavor, and it is shown how the theory presented here can facilitate such interpretation.     

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.     

基于表示学习的开放域中文知识推理

知识库通常以网络的形式被组织起来,网络中每个节点代表实体,而每条连边则代表实体间的关系。为了利用这种网状知识库中的知识,往往需要设计专门的、复杂度较高的图算法。然而这些算法并不能很好适用于知识推理,尤其是随着知识库的知识规模不断扩大,基于网状结构知识库的推理很难较好地满足实时计算的需求。该文使用基于TransE模型的知识表示学习进行知识推理,包括对实体关系三元组中关系指示词以及尾实体的推理,其中关系指示词推理的实验取得了较好的结果,且推理过程无需设计复杂的算法,仅涉及向量的简单运算。另外,该文对原始TransE模型的代价函数进行改进,以更好地适用于开放域中文知识库表示学习。     

A Qualitative Approach to Syllogistic Reasoning

In this paper we present a new approach to a symbolic treatment of quantified statements having the following form Q A's are B's, knowing that A and B are labels denoting sets, and Q is a linguistic quantifier interpreted as a proportion evaluated in a qualitative way. Our model can be viewed as a symbolic generalization of statistical conditional probability notions as well as a symbolic generalization of the classical probabilistic operators. Our approach is founded on a symbolic finite M-valued logic in which the graduation scale of M symbolic quantifiers is translated in terms of truth degrees. Moreover, we propose symbolic inference rules allowing us to manage quantified statements.     

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。     

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。     

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.     

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases

Developed from the dynamic causality diagram (DCD) model,a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph (DUCG) is presented,which focuses on the co...     

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。     

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．     

次协调数据库中的域关系演算

次协调数据库的数据模型是用来处理数据库中两类不确定信息,即不完全信息和不一致信息(矛盾信息)。关系演算语言是表达关系数据模型中的数据操作的一种方式。域关系演算是以域为变量进行的关系演算。文中提出了一种4值的域关系演算来查询次协调数据库,它的语法与普通关系上的2值域关系演算相似,但是这种新的4值语义能够有效的查询不完全信息和不一致信息。这为次协调数据库中的类SQL语言及实现提供了理论依据,进而为次协调数据库的应用打下坚实基础。     

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.     

多媒体数据语义检索的模糊推理机制

研究多媒体数据语义模糊检索的基本原理，设计了Agent知识库的模糊推理机制，提出了后向模糊推理算法和前向模糊推理算法，并进行相关的实践，有效地处理了多媒体数据义复杂性与不确定性的难题，增强了多媒体数据库的检索能力。     

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。