Maintenance Of Expert Systems Research Articles

Implementation of Forward Chaining for Diagnosis of Dengue Hemorrhagic Fever

Insects in the form of mosquitoes are transmitting vectors of several types of diseases that are quite dangerous and need to be watched out for because they can be deadly, such as dengue fever mosquitoes, malaria, elephantiasis and the like. Patients suffering from mosquito-borne diseases must be treated immediately to find out the type of disease they are suffering from and their treatment solutions. The era of digitalization that is growing rapidly, including in the field of medical science, makes the use of information technology to help improve quality services for the community. The analysis used in this research is using the forward chaining method, as a comparison of the overall process of the problems obtained in the investigation and developed using the waterfall paradigm. Forward chaining is optimized because the disease diagnosis process begins by observing the symptoms experienced by a patient and then continues with a tracing process that results in conclusions about the dengue fever suffered. While the waterfall method is used in the development of this expert system because the method can accommodate all stages of the system starting from the analysis of functional and non-functional system requirements, system design, coding, expert system testing and the last stage is expert system maintenance. The data used in this study used data at the Maos Health Center, Cilacap Regency. Testing is carried out using the black box testing method where the expert system is tested on end-users to determine the level of success and usability of the developed system. The questionnaire was given to 76 respondents with the results of the questionnaire: 91.9% indicating the level of user satisfaction, 89.6% indicating the level of effectiveness and 92.2% indicating the level of efficiency of the expert system. The output of this study is the conclusion of the disease suffered by the patient and the solution for handling it.

Conversation System with State Information

Most current approaches to conversation system development invoke a complex set of language parsing rules or development tools difficult for novices to handle and are unable to convincingly simulate advanced natural language features such as topic awareness or conversation thread involvement. This study proposes an alternate approach based on the Ripple Down Rules (RDR) algorithm, presently used to enable more effective maintenance of expert systems. This tree-based algorithm enables a conversation system to travel incrementally deeper into a particular topic, then to switch based on context-dependent information to the correct previously discussed topic – resulting in a highly reusable method of developing conversation systems based around a variety of topics.

A graph-based approach for rule integrity and maintainability in expert system maintenance

Just as conventional software systems have maintenance costs far exceeding development costs, so too do rule-based expert systems. They are frequently developed by an incremental and iterative method, where knowledge and decision rules are extracted and added to the system in a piecemeal manner throughout system evolution. Thus, ensuring the correctness and consistency of the rule base (RB) becomes an important, though challenging task. However, most research work in expert systems has focused on building and validating rule bases, leaving the maintenance issue unexplored. We propose a graph-based approach, called the object classification model (OCM), as a methodology for RB maintenance. An experiment was conducted to compare the OCM with traditional RB maintenance methods. The results show that the OCM helps knowledge engineers retain rule-base integrity and, thus, increase rule-base maintainability.

A regenerative expert system approach for the maintenance of expert systems

When the maintenance of expert systems incorporates not only knowledge bases but also inference engines, algorithms, and user interface modules, the maintenance of the expert systems needs to adopt software maintenance methodologies such as software reuse. We have developed a regenerative expert system approach (REGENESYS) that can modify the rules and program modules of a standard expert system according to the changed specification. We have also proposed a procedure for the approach and illustrated it with the scheduling expert systems for shipbuilding. By adopting the regenerative expert system approach, we can reduce the burden of developing systems for rare occurrences and maintain the capability of modular modification.

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.

Identifying long-term success issues of expert systems

This investigation identifies issues associated with the long-term success of expert systems and the specific nature of maintenance. Open-ended structured interviews were conducted with several expert system developers to assess factors critical to the long-term success of expert systems. The interviews provide an opportunity to examine the developer's perspective. Questions regarding general expert system issues and questions addressing maintenance-specific issues were the basis for the interviews. The interviews were analyzed by three reviewers, and the results were aggregated to create a list of relevant issues associated with long-term success of expert systems. Maintenance, verification and validation, case-based reasoning, realistic user expectations, and problem definition were the five most identified issues. Perceptions of expert systems maintenance were specifically investigated. The dynamic nature of systems was mentioned most often as a critical issue for maintaning expert systems. Results from this investigation provide an awareness of the issues associated with long-term maintenance of expert systems and its relative importance for an expert system development methodology.

A tool for task-based knowledge and specification acquisition

Knowledge acquisition has been identified as the bottleneck for knowledge engineering. One of the reasons is the lack of an integrated methodology that is able to provide tools and guidelines for the elicitation of knowledge as well as the verification and validation of the system developed. Even though methods that address this issue have been proposed, they only loosely relate knowledge acquisition to the remaining part of the software development life cycle. to alleviate this problem, we have developed a framework in which knowledge acquisition is integrated with system specifications to facilitate the verification, validation, and testing of the prototypes as well as the final implementation. to support the framework, we have developed a knowledge acquisition tool, TAME. It provides an integrated environment to acquire and generate specifications about the functionality and behavior of the target system, and the representation of the domain knowledge and domain heuristics. the tool and the framework, together, can thus enhance the verification, validation, and the maintenance of expert systems through their life cycles. © 1994 John Wiley & Sons, Inc.

The client-centred approach: expert system maintenance

Abstract: Developing an expert system is a considerable investment in time and money and yet very little attention has focused on keeping expert systems in regular beneficial use. The problems of maintaining expert systems and the lack of research that this area has attracted are major contributing factors to the continued lack of acceptance of these systems. This paper discusses the maintenance of expert systems within the context of the Client Centred Approach. Although there are many similarities between maintaining expert systems and conventional systems maintenance there is one vital difference—knowledge changes. Consequently an expert system requires regular evolutionary maintenance if its performance is not to be impaired. This paper is intended to promote discussion of maintenance issues within the community. We review some principles and techniques that can assist the maintenance of expert systems and describe some formative work towards a maintenance methodology derived from a case study of maintaining a commercially-available expert system.

Expert systems designed for maintenance

Abstract Constraints for knowledge are proposed as a tool for constructing maintainable expert systems. A simple taxonomy of knowledge constraints is given. It is shown that a special class of knowledge constraints, coupled with the referential integrity constraint, together provide the necessary foundation for a mechanism to substantially automate the expert systems maintenance process provided that the knowledge has been normalised. The normalisation of knowledge is described and its inherent value to the maintenance process is illustrated. An experimental expert systems design and maintenance tool based on this approach has been built.

Logic aids for interviewing experts

Abstract: The excessive time devoted to the development, testing and maintenance of expert systems (ESs) needs to be reduced radically. What is needed is general‐purpose software that can interlace the extracting, structuring, testing, and encoding of knowledge gained from debriefing the expert in any field. LAPS (Logic Aids for Problem Solving) is just such a program and has been successfully used to produce an ES in the domain of submarine diving officers. This automatically‐encoded ES was produced by each expert entering his responses to the LAPS series of strategic‐induction, AI‐jargon‐free queries. Currently, the LAPS prototype has four core sessions or functions: (1) the initial or sample‐solution session; (2) the dechunking or hidden‐knowledge session; (3) the alternatives or completeness‐testing session; and (4) the automatic rule‐production session. After any session the interviewee can use the fourth function of LAPS to produce frame‐laden rules (in M.I, for now; later, in CLIPS). These rules can of course be used to carry out consultation sessions, though fewer of these are needed. LAPS has itself been translated from M.I to C to provide a very fast program on an almost universally available platform — the microcomputer. In accord with the overall technical approach to this project, other successful offline rigorous interviewing functions are being added to LAPS. Other code additions pertain to (a) graphical and other user‐friendly reasoning‐aid enhancements; (b) a series of simpler follow‐up queries; and (c) the fleshing out of many stubs, or functions having intermediate results but as yet no processing.

Implementing Expert Systems in R&D

In a 1987 survey of how Fortune 500 firms were using computer and communications technology in RD over 2000 expert systems were being used daily and another 10,000 were being developed (2). Sales of AI/ES products were expected to reach $650 million in 1989 and be returning $115.5 million on their investment by 1990(3). This led consultant Tom Peters to contend that any company which was not using or considering the use of ES technology was (dangerously) behind the game (4). Since AI/ES accounted for the majority of future applications in the R&D, we decided to conduct an exploratory survey of how AI/ES systems were being used, or being envisioned for future applications, in R&D. The results of that survey were published in 1989 (5). The survey revealed that: 1. The predominant motivation for entry into this area is to capture the expertise of technical experts who are, in the view of the respondents, irreplaceable in their organizations. 2. Virtually all of the respondents were using development shells rather than low-level languages in their implementation. 3. Applications that would lead to more efficient machines and operations, to better control or understanding of complex systems, and those that would capture scarce expertise dominated the list of potential application areas. 4. The most frequently reported payoffs from AI/ES systems included enhanced effectiveness, efficiency, improved service and quality of products, and the preservation and development of expertise. 5. Selling management on the idea and getting the necessary corporate commitment was by far the most significant problem area identified by respondents. Our purpose in this article is to provide additional information and insights into AI/ES systems that will be useful for those anticipating entering this area. Selecting the Problem Although the problems that have benefited from expert systems cover a wide spectrum, there are some general rules that should govern the choice of problems for expert system development, particularly in light of the current state-of-the-art. Four generally agreed-upon principles are: 1. The domain of the problem must be a relatively narrow area in which there is a generally agreed base of knowledge. 2. The system must solve problems an experienced human expert could solve in anywhere from a few minutes to a few hours. 3. The problems must be worth doing, and must be such that top management can be convinced that they are worth the considerable investment the ES approach requires. 4. Solution techniques in ES must be devoid of reliance on common sense and, instead, be based on systematic and logical solution techniques that require very little judgmental inference that cannot be coded as rules. Expert System Development The expert system development process only proceeds to fruition if it gets by the feasibility stage, during which the costs of developing, operating and maintaining the expert system are weighed against the benefits the organization can reasonably anticipate from its implementation. The various costs associated with the development, implementation and maintenance of expert systems should be estimated before embarking on an ES development project (see Table 1). The cost of the development system alone may be several thousand dollars. Once these are estimated, they can be compared to expected benefits in order to determine payback periods, discounted cash flow and other measures of the project's economic feasibility. …

Knowledge net shell (KNS): Petri net based development tool for expert systems

Starting with various design and implementation issues of rule-based expert system, this paper discusses the use of Petri net as an effective and efficient tool for shell based development of expert systems. Petri net approach provides high level interaction with domain expert for automating the construction and maintenance of expert systems. The proposed approach is simple and provides user-friendly environment for query processing and knowledge manipulation.

Specifying an expert system

Abstract The success of numerous expert systems in practical applications warrants a more formal approach to their development and evaluation. Reliability assurance of expert systems requires a methodology for the specification and evaluation of these systems. Expert systems are a new class of software system, but some traditional techniques of software development may be adapted to their construction. However, the specification of an expert system differs from that of a more traditional software program in that parts of the specification are permitted to be only partially described when development starts. Specifications have two important purposes: as contracts between suppliers and clients, and as blueprints for implementation. A specification consists of a problem specification and a solution specification. The problem specification plays the role of contract and states explicitly what the problem to be solved is, and the constraints that the final product must satisfy. The solution specification plays the role of blueprint and has two major aspects: analyzing how a human expert solves the problem, and proposing an equivalent automated solution. We propose an approach to the specification of expert systems that is flexible, yet rigorous enough to cover the important features of a wide range of potential expert system applications. We describe fully each of the components of an expert system specification and we relate specification to the issues of evaluation and maintenance of expert systems.

A philosophical basis for knowledge acquisition

Knowledge acquisition for expert systems is a purely practical problem to be solved by experiment, independent of philosophy. However the experiments one chooses to conduct will be influenced by one's implicit or explicit philosophy of knowledge, particularly if this philosophy is taken as axiomatic rather than as an hypothesis. We argue that practical experience of knowledge engineering, particularly in the long term maintenance of expert systems, suggests that knowledge does not necessarily have a rigorous structure built up from primitive concepts and their relationships. The knowledge engineer finds that the expert's knowledge is not so much recalled, but to a greater or lesser degree “made up” by the expert as the occasion demands. The knowledge the expert provides varies with the context and gets its validity from its ability to explain data and justify the expert's judgement in the context . We argue that the physical symbol hypothesis with its implication that some underlying knowledge structure can be found is a misleading philosophical underpinning for knowledge acquisition and representation. We suggest that the “insight” hypothesis of Lonergan (1958) better explains the flexibility and relativity of knowledge that the knowledge engineer experiences and may provide a more suitable philosophical environment for developing knowledge acquisition and representation tools. We outline the features desirable in tools based on this philosophy and the progress we have made towards developing such tools.

Data dictionary approach to the maintenance of expert systems: The Knowledge Dictionary

The development and use of a Knowledge Dictionary , a tool to facilitate the documentation and maintenance of rule based expert systems, is discussed. The Knowledge Dictionary may be used to record heuristics and their component parts, facts and rule actions, in such a way that a knowledge engineer, or end user, may determine the usage of any part of the knowledge, may easily add new parts, and may run the expert system to determine the effect of the maintenance. The Knowledge Dictionary utilizes the relational data model to store the heuristics in a data form rather than in an executable code form. Use of the relational model provides the knowledge engineer with all the power of relational calculus to interrogate the stored knowledge.

Maintenance of expert systems

It is well known in the business computing community that program and data base maintenance are the largest component of the software system life cycle (Boehm, 1981). Little concern has been raised about the maintenance of knowledge bases of expert systems. The majority of interest in maintenance (which might be classified as being validity over the life cycle of the expert system (Mostow. 1985), and in the maintenance of consistency of a data base (Balzer et al. 1983). One commercial selling strategy of expert systems is that they do not change with time like human knowledge. A person who doesn't change with time however, is not necessarily an intelligent person. An expert system without maintenance is "an old fogey," unable to learn new things. In a preliminary study on now to implement a data base for critical evaluation of atomic and molecular data (Cann and Nicholls, 1980), it was decided to incorporate names of researchers, dates of data added as well as critical notes on methods and recommendations on results. The reasons for this were to be able to compare and update experimental data with time and with new experimental results. But, new results are not always better. Often, older values are obtained through more thorough study then some newer results. There are several different types of errors that can exist in data or knowledge bases.