Representing diagnosis knowledge

This paper considers therepresentation problem: namely how to go from an abstract problem to a formal representation of the problem. We consider this for two conceptions of logic-based diagnosis, namely abductive and consistency-based diagnosis. We show how to represent diagnostic problems that can be conceptualised causally in each of the frameworks, and show that both representations of the same problems give the same answers. This is a local transformation that allows for an expressive (albeit propositional) language for giving the constraints on what symptoms and causes can coexist, including non-strict causation. This non-strict causation can be represented in each frameworkwithout adding special reasoning constructs to either framework. This is presented as a starting point for a study of the representation problem in diagnosis, rather than as an end in itself.     

Representing reusable software

Many methods for representing software components for reuse have been proposed. These include traditional library and information science methods, knowledge-based methods, and hypertext. The paper surveys and categorizes these methods, and discusses systems in which they have been used. A definition and conceptual framework of software reuse representations is proposed that relates these methods to each other and to the software life-cycle.     

Representing and processing grammatical modifiers

Representing English adjectives and adverbs in a logically perspicuous notation (extended semantic networks [17]) and their computational accommodation for natural language comprehension within a state-based paradigm [4] are discussed herewith. Where appropriate, explicit comparisons are made between this representational method and other related approaches such as those in [2], [9], [11], [12], [15], [16], [18].     

Representing partial ignorance

This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example     

Representing preferences using intervals

In this paper we present a general framework for the comparison of intervals when preference relations have to established. The use of intervals in order to take into account imprecision and vagueness in handling preferences is well known in the literature, but a general theory on how such models behave is lacking. In the paper we generalize the concept of interval (allowing the presence of more than two points). We then introduce the structure of the framework based on the concept of relative position and component set. We provide an exhaustive study of 2-point and 3-point intervals comparison and show the way to generalize such results to n-point intervals.     

Representing Software Engineering Knowledge

We argue that one important role that Artificial Intelligence can play in Software Engineering is to act as a source of ideas about representing knowledge that can improve the state-of-the-art in software information management, rather than just building intelligent computer assistants. Among others, such techniques can lead to new approaches for capturing, recording, organizing, and retrieving knowledge about a software system. Moreover, this knowledge can be stored in a software knowledge base, which serves as corporate memory, facilitating the work of developers, maintainers and users alike. We pursue this central theme by focusing on requirements engineering knowledge, illustrating it with ideas originally reported in (Greenspan et al., 1982; Borgida et al., 1993; Yu, 1993) and (Chung, 1993b). The first example concerns the language RML, designed on a foundation of ideas from frame- and logic-based knowledge representation schemes, to offer a novel (at least for its time) formal requirements modeling language. The second contribution adapts solutions of the frame problem originally proposed in the context of AI planning in order to offer a better formulation of the notion of state change caused by an activity, which appears in most formal requirements modeling languages. The final contribution imports ideas from multi-agent planning systems to propose a novel ontology for capturing organizational intentions in requirements modeling. In each case we examine alterations that have been made to knowledge representation ideas in order to adapt them for Software Engineering use.     

Representing UNIX Domain Metaphors

The language used to describe technical domains like UNIX is filled with metaphor. An approach to metaphor, based on theexplicit representation of knowledge about metaphors, has beendeveloped. MIDAS (Metaphor Interpretation, Denotation, andAcquisition System) is a computer program that that has been developedbased upon this approach. MIDAS can be used to representknowledge about conventional metaphors, interpret metaphoric languageby applying this knowledge, and dynamically learn new metaphors asthey are encountered during normal processing.     

智能学习中的知识表示和知识聚类

e-Learning中的智能化学习一直是诸多网络教育研究人员努力去解决而至今尚未有合适解决方案的一个问题。采用传统的导航方式或搜索引擎方式引导学习过程有着自身固有的缺陷。一种新的引导方式是:基于知识指定的表示和组织方式,以知识项和知识交流域(上下文)的映射来引导学习过程。这种方式以知识项得到领域专家认证为前提,通过关联规则对知识进行有效的聚类,给出一系列相关的知识项(相关案例或有关内容),向学习者提供建议性的学习内容,方便了关联学习。     

Representing and Reasoning About XML with Ontologies

The eXtensible Markup Language (XML) has reached a wide acceptance as the relevant standardization for representing and exchanging data on the Web. Unfortunately, XML covers the syntactic level but lacks semantics, and thus cannot be directly used for the Semantic Web. Currently, finding a way to utilize XML data for the Semantic Web is challenging research. As we have known that ontology can formally represent shared domain knowledge and enable semantics interoperability. Therefore, in this paper, we investigate how to represent and reason about XML with ontologies. Firstly, we give formalized representations of XML data sources, including Document Type Definitions (DTDs), XML Schemas, and XML documents. On this basis, we propose formal approaches for transforming the XML data sources into ontologies, and we also discuss the correctness of the transformations and provide several transformation examples. Furthermore, following the proposed approaches, we implement a prototype tool that can automatically transform XML into ontologies. Finally, we apply the transformed ontologies for reasoning about XML, so that some reasoning problems of XML may be checked by the existing ontology reasoners.     

Representing lattices using many-valued relations

This paper provides a representation theorem of lattices using many-valued relations. We show that any many-valued relation can be associated to a unique lattice which is a meet-sublattice of a product of chains. Conversely, to any lattice we can associate a many-valued relation such that its associated lattice is isomorphic to the initial one. Thereby, we obtain a representation theorem of lattices using many-valued relations. Moreover, since several many-valued relations might have the same associated lattice, we give a characterization of the minimal many-valued relation that can be associated to a lattice. We then sketch a polynomial time algorithm which computes such a minimal relation from either a lattice or an arbitrary relation. This representation presents several advantages: it is smaller than the usual binary representation; all known reconstruction algorithms working on binary relation can be used without loss of efficiency; it can be used by existing data mining processes.     

Representing defaults with epistemic concepts

Reasoning about defaults—implications that typically hold, but which may have exceptions—is an important part of commonsense reasoning. We present some parts of a theory of defaults, concentrating on distinctions between various subtle ways in which defaults can be defeated, and on inferences which seem plausible but which are not correct in all cases. To represent this theory in a formal system, it is natural to use the epistemic concept of self-belief. We show how to express the theory by a local translation into autoepistemic logic, which contains the requisite epistemic operators.     

Representing Trees of Higher Degree

This paper focuses on space efficient representations of rooted trees that permit basic navigation in constant time. While most of the previous work has focused on binary trees, we turn our attention to trees of higher degree. We consider both cardinal trees (or k-ary tries), where each node has k slots, labelled {1,...,k}, each of which may have a reference to a child, and ordinal trees, where the children of each node are simply ordered. Our representations use a number of bits close to the information theoretic lower bound and support operations in constant time. For ordinal trees we support the operations of finding the degree, parent, ith child, and subtree size. For cardinal trees the structure also supports finding the child labelled i of a given node apart from the ordinal tree operations. These representations also provide a mapping from the n nodes of the tree onto the integers {1, ..., n}, giving unique labels to the nodes of the tree. This labelling can be used to store satellite information with the nodes efficiently.     

基于符号的装配建模方法研究

在以装配符号的定义实现产品装配设计语义抽象表达的基础上，对产品装配建模过程中装配语义的表达、装配语义与装配约束的转化及维护技术进行研究．提出基于装配符号的约束规则集描述，实现产品装配设计信息的传递，并采用装配符号关系图对产品装配设计语义与约束进行动态维护．通过产品装配信息从抽象到具体、从模糊到精确的转化，实现产品装配设计系统对抽象、模糊、动态关联的产品装配设计信息的表达、传递和维护．     

Representing Animations by Principal Components

In this paper, we present a representation for three-dimensional geometric animation sequences. Different from standard key-frame techniques, this approach is based on the determination of principal animation components and decouples the animation from the underlying geometry. The new representation supports progressive animation compression with spatial, as well as temporal, level-of-detail and high compression ratios. The distinction of animation and geometry allows for mapping animations onto other objects.     

Representing natural language with Prolog

The authors describe the use of Prolog to build a syntactic list structure and a syntactic-semantic structure and to incorporate the semantic structure into a background structure that conveys the meanings of the individual words in the sentence in the context of general work knowledge. This semantic structure is then placed in the context of the sentence being parsed. Rather than rely solely on case grammar to represent the functions of words in a sentence, the authors have extended this technique to include a frame structure (which gives semantic details of the verb and the relations of other words to it) for a sentence's verbs to build the syntactic-semantic structure. Using this frame structure conveys more information than using only the case-grammar approach. The authors then place the syntactic-semantic structure in the context of its background knowledge, using the principles of partitioned networks in which word meanings are placed in a hierarchical structure that represents the background knowledge.<>     

Representing and reasoning about preferences in requirements engineering

The priorities that stakeholders associate with requirements may vary from stakeholder to stakeholder and from one situation to the next. Differing priorities, in turn, imply different design decisions for the system to be. While elicitation of requirement priorities is a well-studied activity, modeling and reasoning with prioritization has not enjoyed equal attention. In this paper, we address this problem by extending a state-of-the-art goal modeling notation to support the representation of preference (??nice-to-have??) requirements. In our extension, preference goals are distinguished from mandatory ones. Then, quantitative prioritizations of the former are constructed and used as criteria for evaluating alternative ways to achieve the latter. To generate solutions, an existing preference-based planner is utilized to efficiently search for alternatives that best satisfy a given set of mandatory and preferred requirements. With such a planning tool, analysts can acquire a better understanding of the impact of high-level stakeholder preferences on low-level design decisions.