Response time satisfaction in a real-time knowledge-based system

At the time being knowledge-based systems are used in almost all life aspects. The main reason for trying to use knowledge-based systems in real-time control is to reduce cognitive load on users (overload), their application proving to be important when conventional techniques have failed or are not sufficiently effective [1]. The development of automated diagnosis techniques and systems can help also to minimize downtime and maintain efficient output. This paper presents some issues of applying knowledge-based systems to real-time control systems. It describes and analyzes the main issues concerning the real-time domain and provides possible solutions, such as a set of requirements that a real-time knowledge-based system must satisfy. The paper proposes a possible architecture for applying knowledge-based techniques in real-time control systems. Finally, a way of employing knowledge-based techniques for extending the existing automatic control and monitoring system for the geothermal plant from the University of Oradea is presented.

Real-time knowledge-based systems (KBSs) are increasingly used to monitor and control real-time processes. Although a KBS may perform well on the average in terms of computation time, it may fail to deliver results while meeting deadlines in some critical cases. This is not acceptable in a hard real-time system where all timing constraints imposed by the environment must be continuously observed by the embedded computer and the failure to meet a single deadline may be catastrophic. Thus the technology for building a new generation of real-time KBSs must be developed before we can rely on these smart systems to perform complex, safety-critical monitoring and control functions in a real-time environment as we currently do on embedded software programs implemented in procedural languages. In this article, we examine some fundamental issues in the design and development of real-time KBSs including (1) response time analysis, and (2) rule-base optimization and synthesis. >

Towards a temporal coherence management in real-time knowledge-based systems

Knowledge-based (KB) technology failed to provide a number of features significant for applications in real-time environments. Real-time KB systems have to provide support for making tradeoffs between processing power, response time, data space, inattention and degradation. There is also a problem of time semantics which have to be included in the knowledge representation.

The application of a supervisory knowledge-based system (KBS) to provide robust multivariable control of a chemical reaction process with reduced conservatism is examined. The supervisory KBS is capable of monitoring the process to detect process and control system faults or deteriorating control system performance due to changes in the process behavior or operating conditions. If a fault or change in performance is detected, the KBS formulates and implements the necessary corrective action. Control loop tuning is emphasized. The underlying mechanisms are discussed and validation results, using a rigorous simulation of the chemical reactor process, are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The results of an assessment of the functionality and performance of tools with different architectural approaches are presented. The goal was to gain insight into the advantages and disadvantages of the various architectures. The tools evaluated in the study were (1) the Automated Reasoning Tool for Information Management (ARTIM), (2) the C Language Integrated Production System (CLIPS), (3) the Knowledge Engineering System (KES), (4) Level 5, and (5) VAX OPS5. Emphasis was on tools implemented in languages other than Lisp. KES and Level 5 were found to be inadequate for an application that required constructive problem solving, but both tools could be expected to provide improved performance for problems based on a heuristic classification approach due to their backward-chaining orientation. On the other hand, ART-IM, CLIPS, and VAX OPS5 might prove less effective for heuristic classification problem solving than for synthesis because they lack support for backward chaining. The results reinforce the fact that all expert system tools have strengths and weaknesses and that no single tool is dominant for a wide spectrum of applications or over a wide range of functionality.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Expert systems are built to solve a wide range of problems in domains such as medicine, science, engineering, manufacturing, education, and many other fields. A Rule-Based Expert System has the following components: the knowledge base, the inference engine, and a user interface that communicates with users. A Decision Tree is a hierarchical data structure consisting of nodes, which store information or knowledge, and edges, which connect the nodes. In this paper, we discuss that the inference or reasoning process of Expert System can be easier to identify by a decision tree structure through a forward reasoning inference or a backward reasoning inference. Forward reasoning inference is data-driven; its reasons for facts to find out what solutions (or conclusions) follow from the facts. However, backward reasoning inference is goal-driven, which needs to find facts that support the hypothesis. A small animal classification example will be discussed. For learning expert system programming effectively, we introduce two software tools and discuss their advantages. Firstly, CLIPS (C Language Integrated Production System) is discussed. CLIPS was designed using C programming language at the NASA/Johnson Space Center with the specific purpose of providing high portability, low cost, and easy integration with external systems. Secondly, an interactive logic programming tool, Visirule is discussed; VisiRule was offered by LPA, a small, private UK company specializing in practical Artificial Intelligence. Finally, we propose a Rubric for Outcome Assessments for learning expert system programming with decision Trees for anyone who is interested in practical expert system programming.

The methods and advantages of integrating knowledge-based and geographical information system techniques for the analysis of provenance and diagenesis in sedimentary basins are demonstrated by examples from the Cheshire Basin located in north-west England. Approximate reasoning techniques to handle the vagueness and uncertainty inherent in a large amount of geological data, knowledge and reasoning are reviewed with particular emphasis on provenance analysis using subjective probability theory, Dempster-Shafer theory and fuzzy logic techniques. Object-oriented methods for analysing diagenetic history in user specified volumes of the basin are discussed.

An intelligent system for multivariate statistical process monitoring and diagnosis

Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations

Swissair, in collaboration with IBM, has developed a knowledge-based client/server system called Swissair Advanced Logical Tarmac Occupation (Salto). Salto is a real-time knowledge-based support system which is used at several airports to allocate aircraft to tarmac stands based on almost continuous updates from an existing host computer. The system alerts users to conflicts, proposes solutions, and plans for adjustments to flight schedules or special circumstances. >

ECDEX: A knowledge-based approach to conceptual design of engineering systems

Review of restoration strategies and a realtime knowledge based approach for bulk power system restoration

Artificial intelligence can assist in industrial control in many ways, but this paper concentrates on the role of knowledge-based systems in the areas of control, detection and diagnosis of faults, scheduling and planning. The paper argues that the application of knowledge-based techniques can provide a unification of these and other activities, and outlines the requirements of knowledge representation and inference techniques that are needed to make such systems a reality. The advances in hardware, including sensors as well as computers, that will be driven by and in turn stimulate the introduction of the knowledge-based industrial control systems are also considered.

Modelling rhetorical legal “logic”—a double syllogism