A FRAME MODEL APPROACH FOR EXPERT AND DATABASE SYSTEM INTEGRATION

The authors propose a general strategy for the design and implementation of an intelligent expert database system using semantic knowledge (integrity constraint) and heuristics (domain-specific knowledge, expert knowledge, approximate knowledge) to search for the answers. The authors present a graph model for representing rules and show how to use knowledge for answering a given query. Also, they suggest some heuristics for deciding the order of subgoals, identifying the useful constraints, and choosing the preferred path. >

Two types of software that make the handling of huge quantities of data easier are described and compared. The first is an expert system which processes extensive and complicated knowledge. It works interactively with the user when it acquires information which is being compared with the facts and relations contained in the knowledge base. The second type of software is a database system which is a record-keeping system that organizes and maintains information and makes it available on demand. Examples of expert systems and database systems in civil and structural engineering are given.

An expert database system is an expert system which is developed on databases using DBMS technology to manage facts and rules. Frame-based expert systems are widely used as the knowledge representation for such expert systems with large knowledge bases. Many systems have the ability to connect to external databases. Facts stored in databases can be loaded into expert system's knowledge base and inference is performed by the inference engine of the expert system. In many cases, such external facts are required several times for each inference. Thus, a lot of communication traffic takes place. This research work present the design and implementation of a frame-based object-relational database system which has a tight coupling between the expert system and the external knowledge base. The external knowledge base also use frame as its knowledge representation. More over, it has its own inference engine so that inference can be perform on the knowledge base side and the results, not only simple facts, are sent back to the expert system for further inference. A medical consultation system is used as an illustrated example an Oracle lOg object relational DBMS is used as the database platform.

In Memoriam: Dr. Clive L. Dym (1942–2016)

The operational reliability of power transformers directly affects the stable and reliable power supply of the power system. According to the available data such as condition assessment, routine test results and maintenance records, a hierarchical architecture and information fusion technology based power transformers life estimation model is presented. Firstly, the model takes advantage of the fault mechanism and the knowledge fitting laws of export system to analyze association property between characteristic parameters and aging information. Then it collects the information representing the insulation aging, reclassify them into several types, and build up a multi-objective evaluating model of power transformers life estimation. After the hierarchy of model is confirmed, we fulfill single characteristic parameter based life estimation and probability density of failure by means of threshold value diagnosis, fuzzy inferences, expert knowledge system, etc. The value of parameters, illation process and evaluation results of the estimation are coded and saved in the database of expert system and enrich the expert knowledge base through deep data mining technology. According to the established evaluation hierarchical architecture, single characteristic parameter models are integrated into the evaluating index system and multi-objective model by analytic hierarchy process (AHP), which can determine the weights at all levels. The multi-parameter power transformer life span evaluating decision model based on information fusion is established so far. The lifetime characteristic parameters from all kinds of sources are integrated to serve as the base layer of the multi-layer decision model, which enhances the efficiency and reliability of this model. It provides a reference for drafting optimal maintenance scheme, achieving the aim of condition based maintenance and power utilities life prediction.

We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts' judgments. SVM is used as an expert system inference engine. The practical list of PSFs and the judgments of GTEDC's (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implemented AASVMES in response to GTEDC's network are evaluated against the GTEDC experts' judgment. Evaluations show that AASVMES has the ability to predict the proper LOA for GTEDC's Utility Management Automation (UMA) system, which changes in relevance to the changes in PSFs; thus providing an adaptive LOA scheme for UMA.

Artificial intelligence in pharmaceutical product formulation: knowledge-based and expert systems

The importance of operators' knowledge in the production of high-quality water and the need for consistent high-level expertise have fueled propositions for the application of computer technology, including knowledge-based expert systems (KBES), as a means of enhancing drinking water treatment operations. With this in mind, the authors have developed CHLOROS, a knowledge-based system which integrates both expert system and hypertext technologies in a WindowsTM environment. Designed as an off-line tool, the system does not directly control operations related to drinking water disinfection, but rather has been intended to assist treatment plant operations personnel. It is believed that CHLOROS will serve as a practical tool for water utilities because it creates a consistent decision-making environment, captures and preserves the expertise of human experts, and reduces the amount of manual effort and time that may be expended by searching through manuals and literature for information that is required to support the choice of control parameters.Key words: knowledge-based systems, expert system, hypertext system, disinfection, chlorination, chloramination.

Computer-Aided Knowledge-Based Monitoring and Diagnostic System for Emulsion Polymerization

Evaluating the success of a knowledge acquisition (KA) task is difficult and expensive. Most evaluation approaches rely on the expert themselves, either directly, or indirectly by relying on data previously prepared with the help of experts. In incremental KA, knowledge base (KB) errors are monitored and corrected by an expert. Thus, during its evolution a record of the knowledge based system (KBS) performance is usually easy to keep. We propose to integrate with the incremental KA process, an evaluation process based on a statistical analysis to estimate the effectiveness of the KBS, as the KBS is actually evolved. We tailor such an analysis for Ripple Down Rules (RDR), which is an effective incremental KA methodology where a record of the KBS performance can be easily derived and updated as new cases are processed by the system. An RDR KB is a collection of rules with hierarchical exceptions, which are entered and validated by the expert in the context of their use. This greatly facilitates the knowledge maintenance task which, characteristically in RDR, overlaps with the incremental KA process. The work in this paper aims to overlap evaluation with maintenance and development of the knowledge base. It also minimises the major expense in deploying the RDR KBS, that of keeping a domain expert on-line during maintenance and the initial period of deployment. The expert is not kept on-line longer than it is absolutely necessary. We use the structure and semantics of an evolving RDR KB, combined with proven machine learning statistical methods, to estimate the added value in every KB update, as the KB evolves. Using these values, the decisionmakers in the organisation employing the KBS can apply a cost-benefit analysis of the continuation of the incremental KA process. They can then determine when this process, involving keeping an expert on-line, should be terminated.

Combining paradigms in knowledge engineering

When processing a query in a conventional database a set of facts are usually returned for an answer. This can be different in a deductive or expert database system. Users may, as before, be interested in a set of facts to answer their query, but in certain queries users are also able to get the answer of a query as a set of formulas (e.g., facts, intensional answers).

The authors review and categorize the research in applications of artificial intelligence (AI) and expert systems (ES) in new product development (NPD) activities. A brief overview of NPD process and AI is presented. This is followed by a literature survey in regard to AI and ES applications in NPD, which revealed twenty four articles (twenty two applications) in the 1990–1997 period. The applications are categorized into five areas: expert decision support systems for NPD project evaluation, knowledge-based systems (KBS) for product and process design, KBS for QFD, AI support for conceptual design and AI support for group decision making in concurrent engineering. Brief review of each application is provided. The articles are also grouped by NPD stages and seven NPD core elements (competencies and abilities). Further research areas are pointed out.

Based on fuzzy mathematics theory, a multi-layer fuzzy comprehensive evaluation system for mechanism schemes of machining center was established using value set instead of fuzzy set, which avoided the problems of constructing membership function, and achieved the quantitative evaluation for mechanism scheme with complex elements preliminarily. The model provided references for the development and establishment of the expert evaluation database system of the mechanism scheme.

The incorporation of interactive interfaces with databases to simulate those found in expert systems and the induction of rules from data are promising approaches toward facilitating the retrieval and interpretation of materials property data, thus combining the benefits of expert system and traditional database technologies. The hybrid system concept of utilizing data as facts and knowledge as rules is described in the context of addressing the need for expanding database utility to meet a broader range of user requirements.