Factors Affecting Expert Systems Implementation by UAE Government

In this paper we investigate the implementation of a cooperative fuzzy expert systems for intelligent recognition of patterns. First we discuss the communication method developed to communicate between the various expert systems on small computers, and then we describe in detail the functions and the structure of each expert system. We also show how fuzzy set theory and fuzzy logic is utilized in the implementation of the system.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

There are few tasks that managers dread as much as employee discipline. Except for cases in which the offense is severe enough to warrant termination, a manager must continue to interact daily with the employee after a punishment has been imposed. When the employee must be fired, the manager often fears that a lawsuit may not be far behind. Thus, when faced with the need to discipline an employee, supervisors might welcome help in figuring out what to do. That is the purpose of the computer expert system called the Supervisor Assistance System, created by the Florida Department of Highway Safety and Motor Vehicles (hereafter referred to as the Florida Department of Motor Vehicles). With the Supervisor Assistance System, a supervisor can first consult with the computer to determine if the employee's offense is actionable under departmental rules. If it is, the supervisor responds to a series of questions about the employee and the details of the offense. The computer then generates one or more recommendations for suggested actions to discipline the employee, along with supportive information from personnel rules. An expert system--one application of artificial intelligence--is a computerized decision-making software that structures expertise in a specific area and emulates human decision-making. The software helps users make a logical decision given its knowledge base drawn from laws, regulations, and human expertise.(1) Public agencies have used expert systems for over a decade, and they are becoming more common for a wide variety of purposes (Leonard-Barton and Sviokla, 1988; Martin, 1991). Social service departments use expert systems to determine eligibility for food stamps or refugee assistance, law enforcement units access solved and unsolved burglary cases through a computerized network to get a profile of possible perpetrators of an unsolved crime, and water testing laboratories apply for state license through an expert system. Although expert systems began as stand-alone computer programs, large public agencies are integrating their expert systems into their information data bases, and thus users may not even recognize their expert system as such (Hedberg, 1995). Most studies of public sector expert systems conclude that their promise is great, as they offer users significant potential benefits: increased productivity, improved service delivery effectiveness, and better problem anticipation (Hadden, 1989; McGowan and Lombardo, 1986; Shangraw, 1987). However, such systems have proven difficult to implement and institutionalize (Hadden, 1989, 204; Hauser and Hebert, 1992; Rubin, 1986). Few empirical studies of expert system implementation have been undertaken to assess what determines the extent to which public-sector decision makers will use an expert system initiated within their organization.(2) This article develops a model of the determinants of the degree to which managers utilize an expert system, and tests the model by examining how managers in the Florida Department of Motor Vehicles react to their expert system. The results offer lessons on how to implement an expert system in an agency so that the organization can achieve the benefits of computer technology. The Florida Department of Highway Safety and Motor Vehicles Supervisor Assistance System The Florida Department of Motor Vehicles developed the Supervisor Assistance System to provide consultations for its Supervisors on handling some 45 different employee offenses that can result in disciplinary actions.(3) (See Appendix 1 for a list of all offenses.) For any type of offense, the Supervisor Assistance System asks questions about the incident under investigation as well as other related information, such as the employee's prior disciplinary record and work history. (As an illustration, Appendix 2 presents the specific questions that the system asks a supervisor for the offense absent without authorized leave. …

The development of an expert menu planning system using the database management system Ingres and the programming language “C” is presented. There are many factors involved in menu planning that tend to make the dietitian's job tedious and long. Therefore, the overall goal of the system is to generate menus for an individual (taking into account his particular nutrient requirements and preferences) in a more efficient and accurate manner than when done manually by the nutritionist. There are five phases involved in developing an expert system [1] : problem identification, conceptualization, formalization, implementation, and testing. These phases encompass such things as: defining the characteristics of the problem, choosing an appropriate knowledge representation scheme, selecting a knowledge engineering tool, building a prototype system, refining the system, and evaluating the final product. Each phase will be addressed to explain the actual implementation of the menu planning system. [1] B.G. Buchanan et al. “Constructing an Expert System.” In F. Hayes-Roth et al. eds., Building Expert Systems. Reading, Massachusetts: Addison-Wesley Publishing Company, Inc., (1983) 127

Limitations on the representation of knowledge, and hence the inference methods, naturally limit the domains of the first-generation expert systems. In response to these limitations, fuzzy expert systems or the second-generation of expert systems provide two essential and unique advantages in the design, development, and implementation of expert systems: (1) fuzzy knowledge representation and (2) fuzzy inference methods. It is suggested that second-generation expert systems be designed and implemented with the expressive powers of representation and improved inference methods based on fuzzy logic. A number of research topics and application areas are identified. >

There are two trends in the software initiatives of the U. S. Department of Defense which will have direct impact on future software development and which suggest a focus for current basic research in software. These trends are 1) the move toward smart weapons, suggesting the use of Expert Systems to control these weapons, and 2) the directive that all software for embedded computer systems be written in Ada. The research focus suggested by these developments is an analysis of the Ada programming language and its associated programming support environment (APSE) to determine how to design and construct an Expert System for natural implementation in Ada.It is well known that Ada can be used as a language for Expert Systems. There now exist several Expert System Shells written in Ada and more are being developed every day. The issue still open to research is how to do this optimally, making the best use of the expressive power of the language. An absurd example will make this clearer. It is also known that it is possible to write an Expert System in COBOL. Doing such would be a very tedious and error prone task, because the expressive power of the COBOL language is focused on commercial applications.Another possibility for writing an Expert System in Ada would be to take an existing Expert System in LISP and translate the code, line for line into Ada. Admittedly this is a plausible task; this author has done some preliminary investigation in this area. However, it is arguable that such an approach will not result in efficient Ada code, for constructs which are natural to LISP might not translate into constructs natural to Ada.While it is difficult to give a precise definition of an Expert System, it is easy to list characteristics which are usually associated with such a system. This paper presents the results of some preliminary research on the implementation of these features by means of some of the structures in Ada, such as Generic Program Units, Packages and Data Encapsulation, which are typical of the language's rich expressive power and which must form the basis of any natural implementation in Ada of an Expert System.One of the features expected in an Expert System is forward and backward chaining, probably implemented with a version of the Rete algorithm. For this reason, this research included writing several small symbolic pattern matching procedures and testing their performance.This research also investigated the use of Ada packages with encapsulated data types to implement a blackboard structure. It was found that a global data structure and appropriate operators could be implemented. As a side result of this work, the frame construct with attached operators and inheritance mechanisms was implemented.The research reported here also addressed the issue of Object Oriented Programming which has become quite popular in LISP programming. Here the match to Ada is quite natural for the latter language itself is also designed to support the Object Oriented Approach. Several LISP textbook examples were translated to the corresponding Ada structure and manipulated with some test programs.Several directions for future research have been generated as a result of this preliminary work. One open question is how to implement concepts originally developed for a language with late binding, such as LISP, in a language with strong typing such as Ada. Another issue concerns the translation of LISP code into Ada code. Early work suggests that it will be more profitable to view the LISP as a high level specification language for an Ada system rather than a program to be translated with literal fidelity. These and several other projects are being pursued and will be reported out in future papers.

Groundwater and soil contamination caused by light nonaqueous phase liquids (LNAPLs) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies, generally classified as ex situ and in situ remediation techniques, have been developed and implemented to clean up the contaminated sites in the last two decades. One of the problems associated with ex situ remediation is the cost of operation. In recent years, in situ techniques have acquired popularity. However, the selection process of the desired techniques needs a large amount of knowledge. Insufficient expertise in the process may result in unnecessary inflation of expenses. In this study, petroleum waste management experts and Artificial Intelligence ( AI) researchers worked together to develop an expert system ( ES) for the management of petroleum contaminated sites. Various AI techniques were used to construct a useful tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation in the project. The expert system was applied to a real-world case study and the results show that the expert system can generate desired remediation alternatives to assist decision-makers. The application case study constitutes partial validation of the prototype expert system.

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

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.

One of the fundamental shortcomings of computers with regard to language materials is the poor basis of information contained in the strings which they recognize The computer merely recognizes strings of letters In computational linguistics much creativity and effort has been spent on the design of computer programs in order to enrich this poor information delivered by computers. No decisive breakthrough, however, could be made, the enriching of the strings cannot be performed automatically as a processing step before other procedures, such as statistical operations are applied In my opinion there are two main reasons for the failure of program packages for linguistic encoding emerge, firstly the theoretical basis for such packages is lacking. Secondly the programming effort is too high to expect results in a short time span. The second reason being a serious drawback for all progress in computational linguistics, we developed the expert system BOBRA for automated language description In this paper we deal with two subjects in particular firstly, the design and implementation of the BOBRA expert system, which we introduce in connection with our views concerning methods for linguistic computing, especially the automated linguistic description of text. After that, we describe the use of the expert system in several applications

The process of constructing a neural-network-based expert system from a given AND/OR inference net is examined. The major purpose of such an approach is to make use of the parallel-processing capabilities of neural networks in implementing expert systems for classifications. In order to provide a clear view of the design process of the proposed neural network expert systems an example of such a classification expert system and its neural network version are given. It is clear from the results that neural networks can be used as an implementational vehicle for expert systems for time-critical applications.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Implementing a fuzzy expert system in an artificial neural network

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 system framework for machine fault diagnosis : Murali Krishnamurthi and Don T. Phillips. Comput. ind. Engng22(1), 67 (1992)

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind.