Strategies for building propositional expert systems

The core of this article is a proof that stratified Horn clause propositional systems are equivalent to and can be efficiently transformed into decision tables by a process closely related to assumption-based truth maintenance. the transformed systems execute much faster and in a bounded time, leading to the possibility of executing real-time expert systems in microseconds on fine-grained parallel computers. One consequence is to simplify the consistency and completeness analysis for such systems, in particular the problem of ambiguity. A deeper consequence is that it makes sense to view these systems as stochastic processes. This, and an analysis of the problem of maintenance of these systems, leads to the conclusion that by and large rule induction approaches are better than rule construction approaches for building them. © 1995 John Wiley & Sons, Inc.     

Framework for building rule-based machine diagnostic expert systems

In the endeavour to build an expert system called XBAK using Personal Consultant Plus for the diagnosis of sophisticated equipment used in microchip manufacturing, a rule-based machine diagnostic expert system architecture was developed. The approach, features and technical implementation of this application-independent problem-solving structure are described. The architecture can be used as a framework for solving similar problems in the area of machine diagnostics.     

Robotics software systems

A robotics software “system” is defined here as one which allows robot users to program robot tasks in terms of key states of the task, instead of manipulator motions. It consists of two subsystems: a language system and a planning system. The language system involves the design of syntax and semantics of a robot programming language whereas the planning system determines specific manipulator movements for a given task defined in a task-level language. This paper describes the major components of a robotics software system and reviews principal research findings in the related aspects including programming languages, manipulator and world modelings, motion planning, and graphic simulation. Underlying research issues are addressed at the end.     

KBase: A customizable tool for building DBase-compatible knowledge-based systems

KBase is a customizable development tool for building knowledge-based systems that are compatible with DBase III tables. Currently, most tools for developing knowledge-based systems do not integrate well with databases. DBase III is the most common database system used in the personal computer environment. KBase is built such that all DBase III commands and functions are supported. Furthermore, customizing and extending KBase is as easy as programming in DBase. This flexibility arises because KBase is built using the “C” programming language and Clipper, a DBase compiler. Using Clipper also allows KBase to use many functions in the Clipper library. In this paper, we describe how KBase's design is conceived. At the end, we also give a brief tour of the program with an implemented example to illustrate its application.     

A use of case-based reasoning technique in building expert systems

Matra Marconi Space France and Aramiihs (Action de Recherche et Application Matra Irit en Interaction Homme Système) laboratory have used and evaluated Case Based Reasoning (CBR) techniques in two projects:

• - The first project is about the development of a system dedicated to help satellites AIT/AIV (Assembly Integration and Test/Validation) test engineers to cope with incidents occurring during test activities. The project is funded by the EGSE System Section of ESTEC (European Space Research and Technology Centre.).

• - The second project is related to the building of a knowledge-based system for diagnosis assistance in AIT/AIV activities of Ariane4 Vehicle Equipment Bay (VEB). The project is financed by internal funding of MMS-F.

In the two projects, CBR technique is neither used the same way nor with the same purpose.

In the first project, CBR technique is used to find out or suggest the cause of an anomaly when an incident appears. Confronted with the occurrence of an incident, the system will refer to its characteristics (test context, symptoms…) that are considered as relevant to retrieve previous similar incidents.

In the second project, CBR technique is combined with Rule Based Reasoning and Model Based Reasoning ones to form the reasoning core of a Hybrid Knowledge Based System. When an incident occurs, the system proposes to test engineers a diagnosis approach based on the combination of different knowledge (coded into rule, cases and models).

Aramiihs is a research unit where engineers from MMS and researchers from the IRIT (Institut de Recherche en Informatique de Toulouse) CNRS (Centre National de la Recherche Scientifique) collaborate on problems concerning new types of man-system interaction.     

Automatic knowledge acquisition tool for irrigation and fertilization expert systems

Knowledge acquisition is commonly regarded as a major obstacle and bottleneck in the process of designing and implementing knowledge base system. Our approach to overcome this problem is to build a task specific knowledge acquisition tool for solving the irrigation and fertilization scheduling problems. The tool was tested using set of cases that was designed to measure the tool flexibility, usability, accuracy, and exception handling.     

A Software Tool for Constructing Traditional Chinese Medical Expert Systems

It is an urgent task to hnplemeut a lot of expert systems to capture the valuable expertise ofexperienced doctors of traditional Chinese medicine.In order to meet the needs,a software tool isdeveloped.It features a unified diagnosis model,a specially designed knowledge representationlanguage and an efficient but effective inference engine.To implement an expert system,it isonly necessary to input the expert's knowledge expressed in knowledge representation languagewithout the design of any additional software.The time and effort required for implementing anexpert system are thus greatly saved.The software is very compact and can run onmicrocomputers e.g.IBM-PC/XT.Two traditional Chinese medical expert systems have beensuccessfully implemented with the tool.     

Complex network tools in building expert systems that perform framing analysis

Framing, in its specific application to media research, is defined as the “central organizing idea for making sense of an issue or conflict and suggesting what is at stake.” It can be found in various disciplines of the social sciences, most notably in political science, psychology, and communication research. Due to the fuzzy nature of frames, identifying them has proven to be quite complex. Here, we perform framing analysis on a corpus of news texts on the population and family planning issue in the Philippines by operating two varying approaches: human-based and computer-assisted. A singular holistic approach to framing is initially implemented where coders/domain experts classify each news text to a specific pre-defined frame. This traditional approach is known to raise serious issues on the reliability and validity of the results mainly due to human’s intrinsic biases. To address such issues, we propose a novel technique that synergically combines the method of Matthes and Kohring (2008) and complex networks approach. In our model, the codings of texts are cast as a network of content analytic variables (CAVs). Our proposed method tackles the clustering issue that MK raised, which plagues framing scholars in the quantitative identification of news frames in texts. Moreover, the research is significant on a societal level as it also aims to gain perspective for reasons on the lack of progress in discussions about suitable population policies in most developing countries like the Philippines.     

Thomas: building Bayesian statistical expert systems to aid in clinical decision making.

Knowledge-based system for classical statistical analysis must separate the task of analyzing data from that of using the results of the analysis. In contrast, a Bayesian framework for building biostatistical expert system allows for the integration of the data-analytic and decision-making tasks. The architecture of such a framework entails enabling the system (1) to make its recommendations on decision-analytic grounds; (2) to construct statistical models dynamically; (3) to update a statistical model based on the user's prior beliefs and on data from, the methodological concerns evinced by, the study. This architecture permits the knowledge engineer to represent a variety of types of statistical and domain knowledge. Construction of such systems requires that the knowledge engineer reinterpret traditional statistical concerns, such as by replacing the notion of statistical significance with that of a pragmatic clinical threshold. The clinical user of such a system can interact with the system at a semantic level appropriate to her fund of methodological knowledge, rather than at the level of statistical details. We demonstrate these issues with a prototype system called THOMAS which helps a physician decision maker interpret the results of a published randomized clinical trial.     

Vision for Robotics: a tool for model-based object tracking

Vision for Robotics (V4R) is a software package for tracking rigid objects in unknown surroundings. Its output is the 3-D pose of the target object, which can be further used as an input to control, e.g., the end effector of a robot. The major goals are tracking at camera frame rate and robustness. The latter is achieved by performing cue integration in order to compensate for weaknesses of individual cues. Therefore, features such as lines and ellipses are not only extracted from 2-D images, but the 3-D model and the pose of the object are exploited also.     

Decision support systems: An expert systems approach

This paper provides a conceptual framework for designing decision support systems (DSS) using an expert systems approach. Currently there is a significant trend towards the use of knowledge-based systems techniques in DSS design, but a comprehensive framework is yet to be proposed. Our paper addresses this problem and presents such a framework. Efforts are currently underway to design, implement and test a system based on this framework.     

Statistical expert systems

Abstract: Two types of expert system which involve statistical expertise are statistical consulting programs and programs which find patterns in databases. Consulting programs can now be built quickly using programming tools. Most expert systems include mechanisms for reasoning under uncertainty. Methods under investigation include fuzzy logic, Dempster-Shafer theory, Bayesian analysis and various ad hoc methods. Learning systems use statistics to infer inductive rules, and statistical reasoning can also be used to evaluate the performance of expert systems. The use of a prototype statistical expert system, XSAMPLE, is demonstrated, as a system to handle a consulting session with a statistically moderately advanced user.     

Evaluating expert systems

Abstract: Evaluation is crucial for improving expert system design and performance. This paper stresses the need for considering system evaluation throughout the development process. It highlights the importance of evaluating system usability and discusses key usability issues. A number of basic evaluation methods are described, including interviews, questionnaires, observation, system logging, user diaries, laboratory experiments and field trials. Finally, the paper looks at evaluating systems within organisations, and assessing other long term effects of expert systems.     

Internet-based expert systems

The Internet offers a large potential for delivery of various information-based services, including the services of intelligent applications. As the availability of the Internet has grown, its value as a medium for the delivery of expert systems in particular has increased. There are now a large number of expert systems available on the Internet, including applications in industry, medicine, science and government. Though the Internet provides several advantages for expert system development, it also presents some special problems. These advantages and disadvantages are explored in more detail in this paper. The paper also presents a review of several Internet-based expert systems with a representative sample of publicly available applications, and a discussion of typical tools for developing Internet-based expert systems. A case study of an Internet-based expert system is presented as well.     

Multi-domain expert systems

Abstract: The current trend in expert system building is domain-specific, i.e. there is one expert system for each problem domain. The increased involvement of computers in the decision-making process will inevitably lead to increased demand for expert systems. Based on the current approach to expert systems building, there will be a further proliferation of domain-specific expert systems. This is because each application area produces an expert system tailored to its requirements. This manner of producing expert systems is inadequate and an increased expectation in the performance of expert systems will eventually call for a new approach to constructing them. This paper examines the growth in the use of expert systems, looks into the limitations and problems associated with present-day domain-specific expert systems and suggests a multi-domain expert system architecture as a solution to the problem of increasingly disjointed domain-specific expert systems resulting from uncontrolled proliferation.     

Categorial expert systems

Abstract: Expert systems can be used to determine some objects or consequences from uncertain knowledge by hierarchical categorization. Categorical representation is psychologically motivated and also offers an explanation of how to deal with uncertain knowledge based on counting during approximate reasoning. It is an alternative to other well‐known uncertainty calculi. A knowledge base which is used during approximate reasoning is represented by a taxonomical arrangement of verbal categories. Priming eases the formation of the final hypothesis, as more exact possible hypotheses are formed. The approximate reasoning is demonstrated on an expert system ‘Jurassic’ from the field of paleontology for the determination of a dinosaur species. It helps the paleontologist to determine creatures from uncertain knowledge. The system is composed of 423 rules arranged in a directed acyclic graph with a depth of 5. This knowledge is represented by a taxonomical arrangement of verbal categories represented by associative memories.     

MAG-EX: A magnetics fabrication expert systems focusing expert systems technology on improving quality control

This paper describes the development and experiences involved with creating a prototype application for the use of expert systems technology in manufacturing, specifically in quality control. The company described in the paper is an electronics manufacturer specializing in power supplies and amplifiers for military and commercial applications. It currently relies heavily on traditional manufacturing information systems technology. Quality control must meet DoD and other regulations and directly affects the bottom-line. As a potential expert system project, management felt as though product quality and the company as a whole could benefit from strategic development in Quality Assurance. The paper discusses the process, the difficulties, and the derived benefits to quality control from the development of an expert system application. Further, the paper reports on additional training benefits derived from the explanation capability of the expert system.