Content and representation effects with reasoning tasks in PROLOG form

Two experiments were carried out to examine human reasoning performance in the context of the logic programming language PROLOG. Two factors, 'content' (familiar versus unfamiliar) and 'representation' (diagrammatic versus PROLOG-like list) were investigated. Subjects answered questions about hierarchical relationships in each condition. A significant interaction was obtained in both experiments, subjects making fewer errors in the familiar-diagram and unfamiliar-list conditions than in the familiar-list and unfamiliar-diagram conditions.

It is hypothesized that a lower percentage of correct responses was given in familiar-list and unfamiliar-diagram conditions because the representation of information prevented successful use of an appropriate reasoning strategy. Working memory limitations provide a basis for understanding constraints on reasoning strategies for solving task questions. These strategies may involve either a serial or a spatial solution process. One strategy may require a larger working memory load than another, depending on the representation and content of the task information. Implications for PROLOG programming instruction are discussed.     

Knowledge representation and reasoning in a software synthesisarchitecture

The knowledge representation and reasoning strategies in an automatic program synthesis architecture called ELF are described. ELF synthesizes computer-aided design (CAD) tools that automatically route wires in VLSI circuits. The design space ELF confronts, requires it to understand various physical technologies, to select an appropriate procedure-level decomposition, to choose algorithms and data structures, to manage any interdependencies, and to generate efficient code. ELF manages the design space using a variety of knowledge sources, including domain-specific knowledge. The manner in which knowledge is used determines the representation method of choice. The effectiveness of these ideas is illustrated via a tour through the synthesis steps for a specific routing tool, and a brief discussion of the performance of the resulting synthetic router as measured against an industrial tool     

Strategic business modeling: representation and reasoning

Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today’s BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making—such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.     

Expressiveness and tractability in knowledge representation and reasoning1

A fundamental computational limit on automated reasoning and its effect on knowledge representation is examined. Basically, the problem is that it can be more difficult to reason correctly with one representational language than with another and, moreover, that this difficulty increases dramatically as the expressive power of the language increases. This leads to a tradeoff between the expressiveness of a representational language and its computational tractability. Here we show that this tradeoff can be seen to underlie the differences among a number of existing representational formalisms, in addition to motivating many of the current research issues in knowledge representation.     

Knowledge representation and reasoning in the design of compositesystems

The design process that spans the gap between the requirements acquisition process and the implementation process, in which the basic architecture of a system is defined, and functions are allocated to software, hardware, and human agents. is studied. The authors call this process composite system design. The goal is an interactive model of composite system design incorporating deficiency-driven design, formal analysis, incremental design and rationalization, and design reuse. They discuss knowledge representations and reasoning techniques that support these goals for the product (composite system) that they are designing, and for the design process. To evaluate the model, the authors report on its use to reconstruct the design of two existing composite systems rationally     

Extending CLIPS to support temporal representation and reasoning

Applications using expert systems for monitoring and control problems often require the ability to represent temporal knowledge and to apply reasoning based on that knowledge. Incorporating temporal representation and reasoning into expert systems leads to two problems in development: dealing with an implied temporal order of events using a non-procedural tool; and maintaining the large number of temporal relations that can occur among facts in the knowledge base. In this paper we explore these problems by using an expert system shell, CLIPS (C Language Integrated Production System), to create temporal relations using common knowledge-based constructs. We also build an extension to CLIPS through a user-defined function which generates the temporal relations from those facts. We use the extension to create and maintain temporal relations in a workflow application that monitors and controls an engineering design change review process. We also propose a solution to ensure truth maintenance among temporally related facts that links our temporal extension to the CLIPS facility for truth maintenance.     

属性RBAC策略的OWL表示和推理

将属性作为授权约束,给出了属性扩展的RBAC模型。提出了一种基于OWL的属性RBAC策略定义和表示方法。该方法支持复杂属性表达式、属性值偏序关系、角色层次关系和约束的定义;在推理机的支持下,可以执行访问控制决策推理,属性表达式支配关系判定和策略知识一致性检测。具体应用案例说明了该方法的可行性。     

Superposition with equivalence reasoning and delayed clause normal form transformation

This paper describes a superposition calculus where quantifiers are eliminated lazily. Superposition and simplification inferences may employ equivalences that have arbitrary formulas at their smaller side. A closely related calculus is implemented in the Saturate system and has shown useful on many examples, in particular in set theory. The paper presents a completeness proof and reports on practical experience obtained with the Saturate system.     

Temporal representation and reasoning in artificial intelligence: Issues and approaches

Time is one of the most relevant topics in AI. It plays a major role in several areas, ranging from logical foundations to applications of knowledge‐based systems. In this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any specific application. The organization of the paper follows the commonly recognized division of the field in two main subfields: reasoning about actions and change, and reasoning about temporal constraints. We give an overview of the basic issues, approaches, and results in these two areas, and outline relevant recent developments. Furthermore, we briefly analyze the major emerging trends in temporal representation and reasoning as well as the relationships with other well‐established areas, such as temporal databases and logic programming. This revised version was published online in June 2006 with corrections to the Cover Date.     

Temporal knowledge representation and reasoning techniques usingtime Petri nets

In this paper, we present temporal knowledge representation and reasoning techniques using time Petri nets. A method is also proposed to check the consistency of the temporal knowledge. The proposed method can overcome the drawback of the one presented in Yao (1994). It provides a useful way to check the consistency of the temporal knowledge.     

Invariant representation of information and information quantity in image-processing tasks

A model of a signal (image) containing the information codes in its own digital virtual memory independent of stretching, packing according to intensity, and other prescribed image transformations is proposed. Virtual memory cells contain pixel values of information representation. Storage elements of virtual memory cells are classified as either fixed or modifiable so that arbitrary variation of the latter does not cause image destruction. The number of modifiable storage elements juxtaposed with image pixels is regarded as the integer-valued information quantity. A comparison of image information quantity estimations is curried out. The applications of the model to image recognition, document protection, and other tasks are discussed. The text was submitted by the author in English. Mikhail V. Kharinov. Born 1953. Graduated from Leningrad State University in 1978. Received candidateís degree in 1993. Senior researcher at and St. Petersburg Institute of Informatics and Automation, Russian, Academy of Sciences. Scientific interests: analysis of numerical information, system of numerical representation, hierarchical data structures, idempotent transformations, unified algorithms for processing of images and audio signals, color transformation of images. Author of 60 papers, including the patents.     

Seven layers of knowledge representation and reasoning in supportof software development

The authors' experience in the Programmer's Apprentice project in applying knowledge representation and automated reasoning to support software development is summarized. A system, called Cake, is described that comprises seven layers of knowledge representation and reasoning facilities: truth maintenance, Boolean constraint propagation, equality, types, algebra, frames, and Plan Calculus. Sessions with two experimental software development tools implemented using Cake, the Requirements Apprentice and the Debugging Assistant, are also included     

Operations on permutations and representation in graph form

Two operations on permutations are defined and some of their properties are illustrated. A particular graph is defined as representation of the permutation set under such operations; this graph consists of strongly connected components only, having at most eight vertices.     

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring).     

Temporal reasoning for manufacturing: A hybrid representation and complexity management

Temporal considerations play a key role in the planning and operation of a manufacturing system. The development of a temporal reasoning mechanism would facilitate effective and efficient computer-aided process planning and dynamic scheduling. We feel that a temporal system that makes use of the expressive power of the integral language and the computational ease of the point language will be best suited to reasoning about time within the manufacturing system. The concept of a superinterval, or a collection of intervals, is used to augment a hybrid point-interval temporal system. We have implemented a reasoning algorithm that can be used to aid temporal decision making within the manufacturing environment. Using the quantitative results obtained by measuring our program's performance, we show how the superinterval can be used to partition large temporal systems into smaller ones to facilitate distributed processing of the smaller systems. The distributed processing of large temporal systems helps achieve real-time temporal decision-making capabilities. Such a reasoning system will facilitate automation of the planning and scheduling functions within the manufacturing environment and provide the framework for an autonomous production facility.     

基于本体的科学效应知识表达和语义推理

针对创新设计领域中科学效应知识的表达和语义扩展方面的不足,分析研究效应、功能、流和参数之间的关系和模型,借助本体将概念间属性和关系扩展到语义层次上,通过本体来更加全面地表达科学效应知识,构建科学效应知识本体模型,包括效应本体、功能本体、流本体和参数本体。在此基础上基于属性构建相应的语义推理规则,进行功能扩展和效应关联,通过语义推理实现科学效应知识的共享和重用。开发一个基于本体的科学效应知识检索系统,验证了此模型和方法的有效性和可行性。     

A framework and computer system for knowledge-level acquisition,representation, and reasoning with process knowledge

The development of knowledge-based systems is usually approached through the combined skills of software and knowledge engineers (SEs and KEs, respectively) and of subject matter experts (SMEs). One of the most critical steps in this task aims at transferring knowledge from SMEs’ expertise to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this process is costly and error prone. Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs. This is especially difficult in the case of complex knowledge types like processes. The analysis of scientific domains like Biology, Chemistry, and Physics uncovers: (i) that process knowledge is the single most frequent type of knowledge occurring in such domains and (ii) specific solutions need to be devised in order to allow SMEs to represent it in a computational form. We present a framework and computer system for the acquisition and representation of process knowledge in scientific domains by SMEs. We propose methods and techniques to enable SMEs to acquire process knowledge from the domains, to formally represent it, and to reason about it. We have developed an abstract process metamodel and a library of problem solving methods (PSMs), which support these tasks, respectively providing the terminology for SME-tailored process diagrams and an abstract formalization of the strategies needed for reasoning about processes. We have implemented this approach as part of the DarkMatter system and formally evaluated it in the context of the intermediate evaluation of Project Halo, an initiative aiming at the creation of question answering systems by SMEs.