Building expert systems: cognitive emulation.

Building Expert Systems: A Structured Bibliography

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

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

The TRANSEPT Family of Expert Systems for the Preliminary Design of Power Networks

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. >

Edamame grade is determined based on edamame quality parameters with several parameters that are quite a lot. The process of deciding edamame grade is relatively complicated and challenging. To determine the edamame grade, an experienced expert in the field is needed. With limited grading experts, the design of expert systems for grading edamame can help users or companies to determine edamame grade. This study aims to design an expert system for determining edamame grade, which can determine the quality of edamame accurately. The Design of an expert system in this research uses the forward chaining method. The stages of designing an expert system include problem identification, knowledge acquisition, building a data flow diagram, compile the database, building a decision tree diagram, and developing a user interface. The results showed that the expert system has been verified and validated well. Verification test results show that all the menu buttons on the expert system application are functioning correctly. Then, the validation results also show that the edamame quality output produced by the expert system is compatible with the expert solution, meaning that the expert system's output data is by following with the results of the decision given by the expert. In testing the validation of determining the quality is appropriate. The results of the verification and validation of the edamame grading expert system show that the expert system has produced satisfactory outcomes. Expert system output is compatible with the solution provided by the expert.

A fundamental shift in the preferred approach to building applied artificial intelligence (AI) systems has taken place since the late 1960s. Previous work focused on the construction of general-purpose intelligent systems; the emphasis was on powerful inference methods that could function efficiently even when the available domain-specific knowledge was relatively meager. Today the emphasis is on the role of specific and detailed knowledge, rather than on reasoning methods. The first successful application of this method, which goes by the name of knowledge-based or expert-system research, was the DENDRAL program at Stanford, a long-term collaboration between chemists and computer scientists for automating the determination of molecular structure from empirical formulas and mass spectral data. The key idea is that knowledge is power, for experts, be they human or machine, are often those who know more facts and heuristics about a domain than lesser problem solvers. The task of building an expert system, therefore, is predominantly one of “teaching” a system enough of these facts and heuristics to enable it to perform competently in a particular problem-solving context. Such a collection of facts and heuristics is commonly called a knowledge base. Knowledge-based systems are still dependent on inference methods that perform reasoning on the knowledge base, but experience has shown that simple inference methods like generate and test, backward-chaining, and forward-chaining are very effective in a wide variety of problem domains when they are coupled with powerful knowledge bases. If this methodology remains preeminent, then the task of constructing knowledge bases becomes the rate-limiting factor in expert-system development. Indeed, a major portion of the applied AI research in the last decade has been directed at developing techniques and tools for knowledge representation. We are now in the third generation of such efforts. The first generation was marked by the development of enhanced AI languages like Interlisp and PROLOG. The second generation saw the development of knowledge representation tools at AI research institutions; Stanford, for instance, produced EMYCIN, The Unit System, and MRS. The third generation is now producing fully supported commercial tools like KEE and S.1. Each generation has seen a substantial decrease in the amount of time needed to build significant expert systems. Ten years ago prototype systems commonly took on the order of two years to show proof of concept; today such systems are routinely built in a few months. Three basic methodologies—frames, rules, and logic—have emerged to support the complex task of storing human knowledge in an expert system. Each of the articles in this Special Section describes and illustrates one of these methodologies. “The Role of Frame-Based Representation in Reasoning,” by Richard Fikes and Tom Kehler, describes an object-centered view of knowledge representation, whereby all knowldge is partitioned into discrete structures (frames) having individual properties (slots). Frames can be used to represent broad concepts, classes of objects, or individual instances or components of objects. They are joined together in an inheritance hierarchy that provides for the transmission of common properties among the frames without multiple specification of those properties. The authors use the KEE knowledge representation and manipulation tool to illustrate the characteristics of frame-based representation for a variety of domain examples. They also show how frame-based systems can be used to incorporate a range of inference methods common to both logic and rule-based systems. "Rule-Based Systems,” by Frederick Hayes-Roth, chronicles the history and describes the implementation of production rules as a framework for knowledge representation. In essence, production rules use IF conditions THEN conclusions and IF conditions THEN actions structures to construct a knowledge base. The autor catalogs a wide range of applications for which this methodology has proved natural and (at least partially) successful for replicating intelligent behavior. The article also surveys some already-available computational tools for facilitating the construction of rule-based knowledge bases and discusses the inference methods (particularly backward- and forward-chaining) that are provided as part of these tools. The article concludes with a consideration of the future improvement and expansion of such tools. The third article, “Logic Programming, ” by Michael Genesereth and Matthew Ginsberg, provides a tutorial introduction to the formal method of programming by description in the predicate calculus. Unlike traditional programming, which emphasizes how computations are to be performed, logic programming focuses on the what of objects and their behavior. The article illustrates the ease with which incremental additions can be made to a logic-oriented knowledge base, as well as the automatic facilities for inference (through theorem proving) and explanation that result from such formal descriptions. A practical example of diagnosis of digital device malfunctions is used to show how significantand complex problems can be represented in the formalism. A note to the reader who may infer that the AI community is being split into competing camps by these three methodologies: Although each provides advantages in certain specific domains (logic where the domain can be readily axiomatized and where complete causal models are available, rules where most of the knowledge can be conveniently expressed as experiential heuristics, and frames where complex structural descriptions are necessary to adequately describe the domain), the current view is one of synthesis rather than exclusivity. Both logic and rule-based systems commonly incorporate frame-like structures to facilitate the representation of large amounts of factual information, and frame-based systems like KEE allow both production rules and predicate calculus statements to be stored within and activated from frames to do inference. The next generation of knowledge representation tools may even help users to select appropriate methodologies for each particular class of knowledge, and then automatically integrate the various methodologies so selected into a consistent framework for knowledge.

This book provides a step by step approach to developing and managing expert systems. The text thoroughly covers all of the proven techniques used in each step of the development process and provides very pragmatic advice covering typical problems faced with building expert systems. One common weakness of many business oriented expert systems texts is that they provide a general listing of expert system examples and usually end up focusing on system that the typical business person cannot relate to. A case in point is Waterman's A Guide to Expert Systems and James Martin's Expert Systems: A Tutorial. Both texts are adequate beginner texts, but Waterman devotes too much attention to Prospector, and expert system for geologists, while Martin provides a lengthy overview of different systems that are available. These exhaustive lists, with descriptions of various expert systems, in a multitude of fields, usually overwhelm the reader with triviality. This information is quickly outdated and rarely provides the reader with any lasting knowledge.

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

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.

This work focuses on the construction of an expert system shell for airborne equipment design. Traditional expert systems are constructed using a single monolithic software program for a specific application. Since the airborne equipment design is critical & complex, it demands the use of expert system technology. Since present aircrafts take in different equipments for different purposes, it is not feasible to think in terms of independently developed monolithic expert system program for each equipment. To overcome these problems, we have designed and developed a complex competent system i.e., a generic component based expert system for airborne equipment design. The main advantage of the component-based approach is that, the knowledge base is not hard coded and these components can be coupled with any domain-specific knowledge bases. The expert system with its expertise knowledge is made to guide the designer by providing design guidelines & testing procedures for the desired equipment. The expert system also audits the design and provides guidelines to modify & to improve the design.

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

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. …

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

최근 제조업에서 제품 설계를 지원하는 디지털 전문가 시스템을 개발하는 사례가 일어나고 있다. 이 시스템은 제조업에서 엔지니어가 프로세스를 통제하고, 생산관리와 시스템 관리 등을 위하여 사용되고 있다. 본 논문에서는 전문가 시스템을 구축하기 위한 온톨로지 기반의 추론 엔진 개발에 대하여 논한다. 전문가 시스템은 한국어를 지원하고 다양한 기능을 가지며, 그래픽한 온톨로지 맵 인터페이스와 퍼지 룰 기능 정의 등의 기능을 갖도록 하였다. 또한, 온톨로지 맵 구축과 온톨로지 기반의 퍼지 추론 방법에 대하여 지식을 표현하는 방법에 대하여 설명한다. Recently, we started a project development of the digital expert system for the product design supporting in manufacturing industry. This digital expert system is used to the engineers in manufacturing industry for the process control, production management and system management. In this paper, we develop the ontology based inference engine shell for building of expert system. This expert system shell included a various functions which of Korean language supporting, graphical ontology map modeling interface, fuzzy rule definition function and etc. And, we introduce the knowledge representation method for the ontology map building and ontology based fuzzy inferencing method.