A survey of French developments in knowledge-based systems

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

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

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.

Research in artificial intelligence (AI) has been concerned with the development of systems that would normally be associated with human intelligence. The domains of application were initially devoted to natural-language processing (NLP), robotics and expert systems. Natural-language processing was concerned with how to communicate with a system through the computer, using text or voice input. Robotics was associated with human behavior in movement in an environment; that entails visual and tactile action. A relatively more recent application can be found in the development of programs that simulate the decision-making behavior of human experts using knowledge of a specific domain of expertise. The term “expert systems” (Harmon & King 1985) was defined by Edward Feigenbaum as “an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution.” Since the seventies, several systems have been developed with this objective. They are called “knowledge-based systems”. However, although some of them contain respectable knowledge bases, their behavior can not be compared to the decision making of a human expert. Sometimes, systems with a sufficient amount of expertise have been called knowledge-based systems with qualification for reasons of modesty.

Research in verification and validation (V&V) of knowledge based systems (KBS) has emerged as a distinct field in the last decade and is intended to address issues associated with KBS quality aspects and to credit such applications to the same degree as conventional applications. Part of the knowledge and techniques developed are now ready to be transferred to industry for the implementation of software certification. Certification is probably the most advanced use of the V&V methods, techniques and knowledge that have been developed so far. The paper approaches the challenging issues of certifying the knowledge base (KB) component of a KBS. KB certification uses a number of different certification methods such as static analysis, testing, inspection, and modeling to assess the quality of the KB component. Although these methods are currently used in KBS development, it is not yet clear how they would be applied by an independent certification organization to arrive at a pass/fail decision. The article discusses the components of KB certification and identifies application areas directly connected with industrial needs.

Knowledge-based Systems and Expert Systems, in particular, are expensive to build and difficult to validate and debug because of their complexity and dynamism. Therefore, it is not easy for knowledge engineer and domain expert to identify the gaps and mistakes in knowledge base. Unit testing is unable to cover validation process at all stages, in many cases manual thorough review of decision process is needed. In this paper we spot main approaches to validation and verification issue and describe a component that helps to debug a knowledge base by visualising execution of rules that derive a particular result. This component is developed for Knowledge-based Systems built on Drools Platform1 and we demonstrate application of this component in a knowledge-based engineering system for structural optical design.

The development of a knowledge-based software package for the analysis and control of electric distribution networks is discussed. A relational knowledge base, implemented in Prolog, is used to insure fast access to the data and an efficient post-reconfiguration update algorithm. An expert system shell DECIDE, is used to interpret human knowledge coded as knowledge clusters, and to provide a menu-driven user interface. The knowledge-based system described here has the following advantages: its expert system shell provides 'backup' and 'explanation' facilities; it can generate all the possible network reconfigurations and recommend the best choice; and the Prolog implementation of the algorithms is easy to maintain. The knowledge base is easy to expand and can be modified during execution. The prototype system demonstrates that artificial intelligence techniques can be used in power system analysis and reconfiguration, and provides a platform on which a base future research on the utility of expert system technology in distribution automation. >

Expert systems (ES) and database systems (DBS) are major components of information systems and important assets to companies. The development of these systems represent users' knowledge in the application systems. As computer technologies evolve, and as users requirements change, there is a need to upgrade these system to meet the new application requirements. To preserve the knowledge of the existing information systems, a methodology for integrating ES and DBS into an expert database system (EDS) is proposed. The integrated EDS is a knowledge based system (KBS) which derives and stores knowledge in a frame model consisting of a class header, attributes, methods and constraints. It extracts the ES rules and DBS data for an application into coupling classes at run time only. The attributes of the coupling classes are matched with synonyms in a synonym table which resolves their naming and semantic conflicts with user assistance in knowledge acquisition. The resultant EDS is a KBS ready for application development.

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. >

The issue on the role of users in knowledge-based systems can be investigated from two aspects: the design aspect and the functionality aspect. Participatory design is an important approach for the first aspect while system adaptability supported by user modelling is crucial to the second aspect. In the article, we discuss the second aspect. We view a knowledge-based computer system as the partner of users' problem-solving process, and we argue that the system functionality can be enhanced by adapting the behaviour of the system to fit the needs of users with different profiles. We emphasise that the notion of user modelling is crucial to realise such kind of flexibility. User modelling will be beneficial to the user, not only through adaptive interfaces, but also through the enhanced system adaptability. In a knowledge-based system, by incorporating user models, searching can be reduced to a smaller portion in the knowledge-base, thus enhancing system functionality. In other words, user modelling is incorporated to realise flexible inference control to achieve system adaptability. An example is provided, and a general conceptual model is sketched. We conclude this paper by emphasising that the design aspect and functionality aspect are complementary. Achieving enhanced functionality through joint efforts of computers and human users indicates a kind of participatory execution of computerised problem-solving or participatory problem-solving.

Summary We present a new approach that uses artificial intelligence to identify thewell-test-interpretation model that best describes the behavior of a reservoir. This knowledgebased (expert) system is composed of carefully extracted rulesand facts for buildup and drawdown test analysis. The rules simulate thereasoning process used by a human expert to identify the appropriateinterpretation model for a well test. The elaborate control needed to simulatea human's reasoning process was simplified with a blackboard architecture. Toexplain the application of the expert system, we use it to analyze a fieldexample. Introduction The accuracy of reservoir properties estimated from well tests depends onprior identification of a well-test-interpretation model that accuratelydescribes the underground system. Identifying the appropriatewell-test-interpretation model is the most fundamental and difficult problemthat an engineer faces when analyzing a pressure-transient test. Recent use ofinvolved mathematical models and techniques has not completely solved thisproblem because choosing an applicable model is not a completely quantitativeprocedure. The choice is based on experience and on a complex reasoning processthat is often deeply buried in the process that is often deeply buried in thehuman expert's mind. If successful, the process of interpreting a well test should provide areservoir model that predicts the behavior of the reservoir being tested. Thisprocess, known as the inverse problem, is characterized by identifying anunknown system (model) by studying the input (rate) and output (pressure)signals. Because a number of models can produce the same output responses froma given input signal, there is no single solution to this problem, suggestingthat a successful interpretation depends on the skills and experience of theinterpreters and their conception of the true model. We developed a knowledge-based system that seeks the appropriateinterpretation model by imitating the reasoning process of human well-testingexperts. The knowledge base uses interactive well-test-analysis and automatichistory-matching modules, and should minimize-and sometimes eliminate-thelack-of-uniqueness problem. Literature Review Although much literature exists on well-test interpretation, work related tothe identification of well-test-interpretation models is very limited. Gringarten discussed the difficulty and complexity of identifying thewell-test model and-validating the analysis. He concluded that, because theprocess involved subjective reasoning, this was an artificial-intelligenceproblem that required an expert system. Using a logical and systematic approach, McVay et al. developed interactivesoftware for well-test analysis. In their work, they indicated that the humanexpert's knowledge needed to be incorporated into an expert system to preventthe user from imposing preconceived modeling ideas on the data. In a recent study, Watson et al. discussed the importance of identifying theinterpretation model as a precondition to successful analysis. They used astatistical approach to choose the applicable model from a group of candidatemodels. Automatic history-matching has been used extensively in recent years toenhance the efficiency and speed of well-test analysis. The interpretationmodel in this case is preselected and a match is sought by changing the modelparameters. This solution, however, is apt to result in misleadinginterpretations because, as indicated earlier, different models can respondsimilarly to a given set of input signals. Erdle et al. described a general interactive software package for well-testinterpretation and introduced expert advice into the well-test-interpretationprocess. In this paper, we introduce a new approach that simplifies the processof determining the applicable well-test-interpretation model. Our approach isbased on artificial-intelligence technology. Problem Solving Model Problem Solving Model Blackboard Architecture. Theknowledge-based architecture consists of three principal components: aknowledge base (facts and rules), a data base (supplied data and solutionstate), and a control (problem-solving) strategy. Although knowledge is the most important part, the control strategy is acritical design part, the control strategy is a critical design component forknowledge-based systems because simulating the human expert's reasoning processrequires that the reasoning steps process requires that the reasoning steps anddomain knowledge be organized such that they can be used efficiently to solvethe problem. A number of problem-solving models have been described in theartificialintelligence literature. JPT P. 654

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.

Potential and present investors in the South African (S.A.) wool industry need up-to-date information on the production and properties of the wool they need for the particular end-products they manufacture or intend to manufacture. This article describes the concept and development of a knowledge-based advisory system on the utilisation of South African wool for the benefit of present and potential investors and other interested parties. The architecture of the system encompasses the concept of distributed systems and the related advantages in its general architecture and within its internal components. It marries both expert and general knowledge-based systems.