Parallel interval-based reasoning in medical knowledge-based system Clinaid

The paper continues a series of papers and a monograph [33], where we have described the conceptual structures as well as the basic architecture of a knowledge-based systemCunaid.     

ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system

This paper describes ROGET, a knowledge-based system that assists a domain expert with an important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.     

A Common Reasoning Model and Its Application in Knowledge-Based System

To use reasoning knowledge accurately and efficiently,many reasoning methods have been proposed.However,the differences in form among the methods may obstruct the systematical analysis and harmonious integration of them.In this paper,a common reasoning model JUM(Judgement Model)is introduced.According to JUM,a common knowledge representation form is abstracted from different reasoning methods and its limitation is reduced.We also propose an algorithm for transforming one type of JUMs into another.In some cases,the algorithm can be used to resolve the key problem of integrating different types of JUM in one system.It is possible that a new architecture of knowledge-based system can be realized under JUM.     

Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system

This study intends to propose a hybrid Case-Based Reasoning (CBR) system with the integration of fuzzy sets theory and Ant System-based Clustering Algorithm (ASCA) in order to enhance the accuracy and speed in case matching. The cases in the case base are fuzzified in advance, and then grouped into several clusters by their own similarity with fuzzified ASCA. When a new case occurs, the system will find the closest group for the new case. Then the new case is matched using the fuzzy matching technique only by cases in the closest group. Through these two steps, if the number of cases is very large for the case base, the searching time will be dramatically saved. In the practical application, there is a diagnostic system for vehicle maintaining and repairing, and the results show a dramatic increase in searching efficiency.     

Modeling diagnostic reasoning: a summary of parsimonious covering theory

Parsimonious covering theory is a formal model of diagnostic reasoning. Diagnostic knowledge is represented in the theory as a network of causal associations, and problem-solving is represented in algorithms that support a hypothesize-and-test inference process. This paper summarizes in informal terms the basic ideas in parsimonious covering theory.     

Parallel processing in knowledge-based diagnostic systems

This paper highlights the use of the parallel processing concept in knowledge-based diagnostic systems. A MIMD machine connected in a cubic mesh fashion using Parlog has been suggested to implement such systems. An algorithm supporting the concurrent execution of multiple conflict set rules of the same production system program is presented. A specific application to communication systems maintenance utilizing these principles has been discussed.     

Student modelling by a knowledge-based system

ELECTRE is a project to build an intelligent tutoring system for learning basic electricity. This paper describes a student model based on the student's cognitive processes. This model includes, for each student, his/her domain knowledge and the specific heuristics. Moreover, it uses meta-knowledge of problem solving. This model is simulated by a knowledge-based system that controls the solving processes by meta-rules. A case study is presented.     

KBMS: a knowledge-based system for modeling software systemspecifications

Recent research results in software engineering have shown that the process of formalizing and automating software specification activity is moving towards knowledge-based specification systems. A modeling approach and a knowledge-based system that assist in the requirement acquisition, development, and maintenance of such software system specifications are presented     

Behavioral Petri nets: a model for diagnostic knowledgerepresentation and reasoning

Some of the most popular approaches to model-based diagnosis consist of reasoning about a model of the behaviour of the system to be diagnosed by considering a set of observations about such a system and by explaining it in terms of a set of initial causes. This process has been widely modeled via logical formalisms essentially taking into account declarative aspects. In this paper, a new approach is proposed, where the diagnostic process is captured within a framework based on the formalism of Petri nets. We introduce a particular net model, called Behavioral Petri Net (BPN), We show how the formalization of the diagnostic process can be obtained in terms of reachability in a BPN and can be implemented by exploiting classical analysis techniques of Petri nets like reachability graph analysis and P-invariant computation. Advantages of the proposed methods, like suitability to parallel processing and exploitation of linear algebra techniques, are then pointed out.     

KIMS: knowledge-based image management system

A knowledge-based image management system called KIMS is developed for storage and retrieval of images. The aim of the system is to overcome some of the limitations inherent in Micro-IDBMS, a microcomputer-based image database management system. These limitations include abrupt failure and the inability to capture the concept of an image. Abrupt failure may occur during retrieval when there are no images that have the specified attribute value. The inability to capture the concept of an image is related to the general idea that an image is a representation.

KIMS basically consists of an image knowledge base and an inference mechanism, in addition to Micro-IDBMS. The paper describes the rolé of image knowledge and presents the design and implementation details of KIMS.     

TUNEX: a knowledge-based system for performance tuning of the UNIXoperating system

TUNEX, an expert system developed for performance tuning of the UNIX operating system, is described. TUNEX was developed on UNIX system V. It uses the properties, commands and utilities of this version. The tuning activities it is concerned with include: (1) adjusting operating system tunable parameters, such as number of disk buffers; (2) running maintenance routines, i.e. reorganizing file systems; (3) developing operation rules, such as off-peak hour runs of backups; and (4) modifying hardware, buying an additional disk drive. The structure of TUNEX is presented and performance analysis modules which provide quantitative information to this tool are briefly described. The overhead in the resource usage introduced by the performance monitoring and tuning tool itself is discussed; the author points to the areas in which additional resources are required by TUNEX     

Designing a knowledge-based system for benchmarking: A DEA approach

First developed by Xerox in 1979, benchmarking provides measurement and comparison to improve processes and achieve higher performance. Benchmarking has proven a powerful tool for total quality management and process improvement. Successful benchmarking implementation is based on an effective benchmarking tool. To effectively implement benchmarking processes, this work proposes an integrated framework for the benchmarking tool and knowledge-based system using the data envelopment analysis (DEA) method, and then develops an intellectual benchmarking knowledge-based system (BKBS) for benchmarking, performance evaluation and process improvement. Accordingly, this work illustrates how the benchmarking knowledge-based system (BKBS) is implemented in a medical center. This system can help determine the particular benchmarking partners to evaluate the relative efficiency and fill the gaps between the benchmarking partners in the healthcare industry. Finally, the intellectual benchmarking knowledge-based system offers a very fast way to implement the benchmarking processes.     

Modal Change Logic (MCL): Specifying the reasoning of knowledge-based systems

We investigate the formal specification of the reasoning process of knowledge-based systems in this paper. We analyze the corresponding parts of the KADS specification languages KARL and (ML)² and deduce some general requirements. The essense of these languages is that they integrate a declarative specification of inferences with control information. The languages differ in the way they achieve this integration and each of them has shortcomings. We propose a unifying semantical framework that integrates the core of the different solutions and overcomes their problems. We define a semantics and axiomatization with the Modal Change Logic (MCL). The main contribution of the paper is not to introduce yet another specification language. Instead we aim at four goals: (1) defining a framework for describing the dynamic reasoning behavior of knowledge-based systems which integrates existing approaches; (2) defining a semantics for the specification of the dynamic reasoning behavior of a knowledge-based system within the states as algebras setting that overcomes several shortcomings and ad hoc solutions of existing approaches; and (3) providing an axiomatization that enables the development of mechanized proof support. (4) Through conceptual and semantical clarity, we investigate the relationships to similar work in software engineering and database engineering opening possibilities for further cross-fertilization of these fields.     

Formalizing dialectical explanation support for argument-based reasoning in knowledge-based systems

The concept of explanation has received attention from different areas in Computer Science, particularly in the knowledge-based systems and expert systems communities. At the same time, argumentation has evolved as a new paradigm for conceptualizing commonsense reasoning, resulting in the formalization of different argumentation frameworks and the development of several real-world argument-based applications. Although the notions of explanation and argument for a claim share many common elements in knowledge-based systems their interrelationships have not yet been formally studied in the context of the current argumentation research in Artificial Intelligence. This article explores these ideas by providing a new perspective on how to formalize dialectical explanation support for argument-based reasoning. To do this, we propose a formalization of explanations for abstract argumentation frameworks with dialectical constraints where different emerging properties are studied and analyzed. As a concrete example of the formalism introduced we show how it can be fleshed out in an implemented rule-based argumentation system.     

Developing an explanation component for a knowledge-based system: Discussion

The articles in this special issue cover a wide range, and represent many different conceptions of “explanation” and what explanation research should be about. In this short discussion article, I will address some of the implications of this work for those trying to develop a practical explanation system. In particular I will focus on how explanation needs are determined, how detailed explanation content is chosen, and how the explanations provided relate to the problem solving activities of the system.     

Causal models for diagnostic reasoning

Theories of qualitative physics are crucial in developing knowledge-based systems in engineering. Basic models for diagnostic reasoning are based on causal connections between the system parameters. In this paper a knowledge representation and problem solving technique is presented as a ground-laying step. The technique is based on the concepts introduced by Iwasaki and Simon⁹ and employs the methods of analysis from the field of control engineering. The causal analysis of systems with feedback is done by using knowledge about their block diagrams. A constraint representing the feed-forward path is modified in order to eliminate the feedback parameter. This approach supports the confluence heuristic in de Kleer and Brown's qualitative physics⁴. Causal dependencies that partially describe the behaviour of a system are used to generate a search space for faulty components. This approach is based on reasoning about counterfactuals using modal categorizations, as proposed by Nicolas Rescher and Herbert Simon¹². The scope of application of this method in real-time monitoring and diagnosis of large industrial processes is discussed.