AUTOMATED KNOWLEDGE ACQUISITION: OVERCOMING THE EXPERT SYSTEM BOTTLENECK

The artificial intelligence (AI) discipline of machine learning offers the best opportunity for alleviating the critical problem of acquiring the knowledge base necessary for expert systems. This paper examines the characteristics of such tasks and identifies a number of weaknesses with several dominant AI approaches. Genetic algorithms (GAs) are a probabilistic search technique based on the adaptive efficiency of natural organisms and offer an alternative which addresses the weaknesses in conventional methods. This paper describes the implementation of ADAM, a GA driven classifier, and compares the quality of the rules it generates to those of alternative induction techniques on a simulated decision problem

Automated knowledge acquisition tool for identification of generic tasks

Recent research in the Knowledge Acquisition (KA) field, centers on defining a formal methodology for the KA process. This research includes the following goals: automating the KA process to decrease the KA time constraint; applying psychological techniques to extract the underlying structure of the expert\u27s knowledge; and defining expertise in terms of generic tasks to yield possible knowledge organizations and strategies for the implementation of the expert system. This thesis provides an overview of the benefits and concerns of an automated KA system, psychological scaling techniques as they apply to KA, and the relevance of generic tasks. A generic task defines a knowledge type and organization, and a control strategy that characterizes a component of an expert system. This thesis also includes the design and implementation of a Knowledge Acquisition Tool for Identification of Generic Tasks. This tool provides an interface to the expert for the initial KA encounter. Using psychological techniques, the tool extracts a list of the main concepts of expertise, and elicits a rating from the expert comparing the similarity of each of these concepts to generic task concepts. The results become inputs to a clustering technique that organize the concepts into the generic tasks. The result of any concepts that do not cluster could identify a previously undefined generic task. The implementation is in the C language, accessing the FASTCLUS procedure of the SAS software package for VAX hardware

Automated knowledge acquisition for knowledge-based systems: KE-KIT

Despite recent progress, knowledge acquisition remains a central problem for the development of intelligent systems. There are many people throughout the world doing studies in this area. However, very few automated techniques have made it to the market place. In this light, the idea of automating the knowledge acquisition process is very appealing and may lead to a break through. Most (if not all) of the approaches and techniques concerning intelligent, expert systems and specifically knowledge-based systems can still be considered in their infancy and definitely do not subscribe to any kind of standards. Many things have yet to be learned and incorporated into the technology and combined with methods from traditional computer science and psychology. KE-KIT is a prototype system which attempts to automate a portion of the knowledge engineering process. The emphasis is on the automation of knowledge acquisition activities. However, the transformation of knowledge from an intermediate form to a knowledge -base format is also addressed. The approach used to automate the knowledge acquisition process is based on the personal construct theory developed by George Kelly in the field of psychology. This thesis gives and in-depth view of knowledge engineering with a concentration on the knowledge acquisition process. Several issues and approaches are described. Greater details surrounding the personal construct theory approach to knowledge acquisition and its use of a repertory grid are given. In addition, some existing knowledge acquisition tools are briefly explored. Details concerning the implementation of KE-KIT and reflections on its applicability round out the presented material

The integration of automated knowledge acquisition with computer-aided software engineering for space shuttle expert systems

A prediction was made that the terms expert systems and knowledge acquisition would begin to disappear over the next several years. This is not because they are falling into disuse; it is rather that practitioners are realizing that they are valuable adjuncts to software engineering, in terms of problem domains addressed, user acceptance, and in development methodologies. A specific problem was discussed, that of constructing an automated test analysis system for the Space Shuttle Main Engine. In this domain, knowledge acquisition was part of requirements systems analysis, and was performed with the aid of a powerful inductive ESBT in conjunction with a computer aided software engineering (CASE) tool. The original prediction is not a very risky one -- it has already been accomplished

Computer-assisted knowledge acquisition for hypermedia systems

The usage of procedural and declarative knowledge to set up the structure or 'web' of a hypermedia environment is described. An automated knowledge acquisition tool was developed that helps a knowledge engineer elicit and represent an expert's knowledge involved in performing procedural tasks. The tool represents both procedural and prerequisite, declarative knowledge that supports each activity performed by the expert. This knowledge is output and subsequently read by a hypertext scripting language to generate the link between blank, but labeled cards. Each step of the expert's activity and each piece of supporting declarative knowledge is set up as an empty node. An instructional developer can then enter detailed instructional material concerning each step and declarative knowledge into these empty nodes. Other research is also described that facilitates the translation of knowledge from one form into a form more readily useable by computerized systems

Reasoning-Supported Quality Assurance for Knowledge Bases

The increasing application of ontology reuse and automated knowledge acquisition tools in ontology engineering brings about a shift of development efforts from knowledge modeling towards quality assurance. Despite the high practical importance, there has been a substantial lack of support for ensuring semantic accuracy and conciseness. In this thesis, we make a significant step forward in ontology engineering by developing a support for two such essential quality assurance activities

First CLIPS Conference Proceedings, volume 1

The first Conference of C Language Production Systems (CLIPS) hosted by the NASA-Lyndon B. Johnson Space Center in August 1990 is presented. Articles included engineering applications, intelligent tutors and training, intelligent software engineering, automated knowledge acquisition, network applications, verification and validation, enhancements to CLIPS, space shuttle quality control/diagnosis applications, space shuttle and real-time applications, and medical, biological, and agricultural applications

Increasingly automated procedure acquisition in dynamic systems

Procedures are widely used by operators for controlling complex dynamic systems. Currently, most development of such procedures is done manually, consuming a large amount of paper, time, and manpower in the process. While automated knowledge acquisition is an active field of research, not much attention has been paid to the problem of computer-assisted acquisition and refinement of complex procedures for dynamic systems. The Procedure Acquisition for Reactive Control Assistant (PARC), which is designed to assist users in more systematically and automatically encoding and refining complex procedures. PARC is able to elicit knowledge interactively from the user during operation of the dynamic system. We categorize procedure refinement into two stages: diagnosis - diagnose the failure and choose a repair - and repair - plan and perform the repair. The basic approach taken in PARC is to assist the user in all steps of this process by providing increased levels of assistance with layered tools. We illustrate the operation of PARC in refining procedures for the control of a robot arm

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed