Abstract: Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems This paper presents a description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks

Abstract: The outbreak of COVID-19

Abstract: From the Publisher: This new edition combines a thorough, balanced treatment of theory and practice with a complete package of CLIPS 6.0 software tools for developing expert systems. It features a balanced blend of expert systems theory and practice; a detailed presentation of CLIPS Version 6.0, a rule-based programming language for expert systems design; and an IBM PC 3 1/2'' disk which contains the complete CLIPS 6.0 executable shell and sample programs for developing expert systems.

Abstract: A critical decision method is described for modeling tasks in naturalistic environments characterized by high time pressure, high information content, and changing conditions. The method is a variant of a J.C. Flanagan's (1954) critical incident technique extended to include probes that elicit aspects of expertise such as the basis for making perceptual discriminations, conceptual discriminations, typicality judgments, and critical cues. The method has been used to elicit domain knowledge from experienced personnel such as urban and wildland fireground commanders, tank platoon leaders, structural engineers, design engineers, paramedics, and computer programmers. A model of decision-making derived from these investigations is presented as the theoretical background to the methodology. Instruments and procedures for implementing the approach are described. Applications of the method include developing expert systems, evaluating expert systems' performance, identifying training requirements, and investigating basic decision research issues. >

Abstract: Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system. CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Implementations of the CN2, ID3, and AQ algorithms are compared on three medical classification tasks.

Abstract: Expert systems and knowledge-based systems have stemmed from research into artificial intelligence (AI), i.e. the mimicking of human thought processes in a computer. It was proposed that an intelligent computer system should contain some software whose purpose was to control thoughts (the inference engine) and other software representing knowledge (the knowledge base). Computer systems built with this type of software architecture are known as knowledge-based systems. Knowledge-based systems can be used in order to carry out specialized human tasks, mimicking the behaviour of experts in specialized areas. The term expert system was coined to describe such systems. >

Abstract: A precise definition is provided for general configuration tasks. Two important assumptions are identified: (i) functional architecture and (ii) key component per function. A domain-independent model is presented based on these assumptions. These assumptions are shown to be both useful and tenable in real domains. They are useful because they limit the complexity of the general configuration task, determine the basic knowledge needed for solving a configuration task, and enable more efficient problem solving methods. Ideas are presented both for representing the knowledge and controlling the search. Some of these ideas were originally implemented in the Cossack expert system.

Abstract: Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell - for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components-- nodes and arcs--as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an exam ple of a real world application, MUNIN an expert system for electromyography is discussed.

Abstract: The ability of knowledge-based expert systems to facilitate the automation of difficult problems in process engineering that require symbolic reasoning and an efficient manipulation of diverse knowledge has generated considerable interest recently. Rapid deployment of these systems, however, has been difficult because of the tedious nature of knowledge acquisition, the inability of the system to learn or dynamically improve its performance, and the unpredictability of the system outside its domain of expertise. This paper proposes a neural-network-based methodology for providing a potential solution to the preceding problems in the area of process fault diagnosis. The potential of this approach is demonstrated with the aid of an oil refinery case study of the fluidized catalytic cracking process. The neural-network-based system successfully diagnoses the faults it is trained upon. It is able to generalize its knowledge to successfully diagnose novel fault combinations it is not explicitly trained upon. Furthermore, the network can also handle incomplete and uncertain data. In addition, this approach is compared with the knowledge-based approach.

Abstract: Suppose we are given some collection of confirmed data items (information). We can assume that the information is represented as a set P of propositions of predicate logic. We look at P and try to use some sort of inference mechanism to extract more information out of P.

Abstract: An expert-systems-based automated design approach for analog circuits is presented The approach uses both formal and intuitive knowledge in the design process A prototype design environment, BLADES, which uses a divide and conquer solution strategy, has been successfully implemented and is currently capable of designing a wide range of subcircuit functional blocks as well as a limited class of integrated bipolar operational amplifiers BLADES is believed to be the first successful design expert system in the analog design domain It uses different levels of abstraction depending on the complexity of the design task under consideration The importance of the abstraction level lies in the fact that once design primitives are defined, the problem of extracting the knowledge (design rules) become less complex Two design examples are given to demonstrate the viability and versatility of the knowledge-based design technique as an analog design tool None of the circuits designed and tested using BLADES were unstable >

Abstract: This book is the first detailed account of the development of a complex and successful expert system based on deep and qualitative knowledge. It shows how the qualitative modeling approach, using logic based representations and machine learning techniques, can be used to construct knowledge bases whose complexity is far beyond the capability of traditional, dialogue based techniques of knowledge acquisition.The relevant techniques are demonstrated in full detail in the building of Kardio, a medical expert system model of the human heart designed for the diagnosis of cardiac arrhythmias. Kardio's performance is estimated by cardiologists to be equivalent to that of a specialist of internal medicine (not a cardiologist) who is highly skilled in the reading of ECG recordings, and it can be used as a diagnostic tool in ECG interpretation. It may also be used for instruction in electrocardiography.The authors show how the model was compiled, by means of qualitative simulation and machine learning tools, into various representations that are suited for particular expert tasks. They investigate a hierarchical organization of a qualitative model and outline an experiment whereby the construction of a deep model is automated by means of machine learning techniques. The book contains a complete model of the electrical system of the heart that can be used to further development in this area of applications.Ivan Bratko, author of "Prolog Programming for Artificial Intelligence, "is a professor of computer science at E. Kardelj University and leads the AI laboratory at the Jozef Stefan Institute in Ljubljana, Yugoslavia. Igor Mozetic and Nada Lavrac are researchers at the institute.

Abstract: A general purpose expert system architecture for diagnosing faults in any one of a plurality of machines includes a machine information database containing information on characteristics of various components of the machines to be diagnosed and a sensory input database which contains vibration data taken at predetermined locations on each of the machines. The system knowledge base contains a plurality of general rules that are applicable to each of the plurality of machines. The generality of diagnosis is accomplished by focusing on components that make up the machine rather than individual machines as a whole. The system architecture also permits diagnosis of machines based on other parameters such as amperage, torques, displacement and its derivatives, forces, pressures and temperatures. The system includes an inference engine which links the rules in a backward chaining structure.

Abstract: From the Publisher: Presents a model for intelligent databases based on five information technologies: databases, object-oriented programming, expert systems, hypertext, and text management. This self-contained treatment actually shows how to construct an intelligent database on a microcomputer by building a shell much like the shells used to construct expert systems. A realistic intelligent database example is throughout the book to illustrate ideas being discussed.

Abstract: A viewpoint on expert systems is developed that considers the qualitative nature of knowledge encoded in such programs. It is maintained that all knowledge bases contain models of systems in the world. Reasoning involves sequences of tasks (for example, monitoring and diagnosis) by which an understanding or model of specific situations is related to action plans. Programs use a simple repertoire of qualitative modeling techniques commonly called knowledge representations. The situation-specific-model concept makes concrete exactly what programs know and how to describe problem solving in terms of model-manipulation operators. The viewpoint is exemplified for the particular case of medical diagnosis. A historical perspective shows how AI's concern with adaptiveness and the rationality of the autonomous agent emphasizes the role of models as what a problem-solver knows. It is suggested that this has been to the detriment of understanding the primary characteristic of knowledge in terms of models that partition the world, viewing it selectively and making it coherent for some purpose. >

Abstract: WORK ON EXPERT SYSTEMS hasreceived extensive attention recently, prompting growing interest in a range of environments. Much has been made of the basic concept and of the rule-based system approach typically used to construct the programs. Perhaps this is a good time then to review what we know, assess the current prospects, and suggest directions appropriate for the next steps of basic research. I’d like to do that today, and propose to do it by taking you on a journey of sorts, a metaphorical trip through the State of the Art of Expert Systems. We’ll wander about the landscape, ranging from the familiar territory of the Land of Accepted Wisdom, to the vast unknowns at the Frontiers of Knowledge I guarantee we’ll all return safely, so come along. . . The itinerary Our trip has three basic purposes in mind. I want first to assess and calibrate the Accepted Wisdom, second to understand its limitations, and third to characterize the nature of the research that will produce the next generation of systems. The talk is correspondingly divided into three parts. We begin with a tour through the Land of Accepted Wisdom, where we survey the field, trying to assess what’s known about building and using these systems. What do we know and what can we do with that

Abstract: level of the hypothesis is referred to as an expectation-lank. The node at the end of an expectation-link is a model-based hypothesis element, and the link represents support from above (i e , the reason for proposing the hypothesis element is to be found at the higher level). 2. ,4 link which goes in the opposite direction, from lower levels of abstraction to higher, is referred to as a reduction-/ink The node at the end of a reduction-link is a data-based hypothesis element, and the link represents support from below (i e , the reason for proposing the hypothesis element is to be found at a lower level). An example of hypothesis elements generated by the KSs are shown in Figure 4 Knowledge about how to use other knowledge: Since the strategy is opportunistic, the program must know when an opportunity for further interpretation has arisen and how best to capitalize on the situation. In HASP/SIAp this type of knowledge is made explicit as will be explained in the following section. How the knowledge is organized and used. How well an Expert System performs depends both on the competence of the KSs and on the appropriate use of these KSs Thus, the primary considerations in the design of Expert Systems revolve around the availability and the quality of the KSs, and the optimal utilization of these KSs When and how a KS is used depends on its quality and its relevancy at any given time The relevance of a KS depends on the state of the CBH. The control mechanism for KS selection needs to be sensitive to, and be able to adjust to, the numerous possible solution states which arise during interpretation. Given this viewpoint, what is commonly called a “control strategy” can be viewed as another type of domain-dependent knowledge, albeit a high level one. Organizing the knowledge sources in a hierarchy is an attempt to unify the representation of diverse knowledge needed for the interpretation task In a hierarchzcally organized control structure, problem solving activities decompose into a hierarchy of knowledge needed to solve problems. On the lowest level is a set of knowledge sources whose charter is to put inferences on the CBH We refer to KSs on this level as Speczalists. At the next level there are KS-activators that know when to use the various Specialists. On the highest level a StrategyKS analyzes the current solution stat’e to determine what information to analyze next. The last activity is also known as focussing-of-attention Kinds of knowledge represented. There are several kinds of knowledge used in HASP/SUP, each represented in a form that seems the most appropriate. Knowledge about the environment: The program must know about common shipping lanes, location of arrays and their relation to map coordinates, and known maneuver areas. This knowledge is represented in procedures that. compute the necessary information. Knowledge about vessels: All the known characteristics about vessel types, component parts and their acoustic signatures, range of speed, home base, etc , are represented in frame-like structures. These constitute the static knowledge used by rules whenever a specific class of vessels is being analyzed. In addition, when some vessel class is inferred from a piece of data, detailed information is available to help make that hypothesis more credible. The use of this information by model-driven KSs reduces the amount of computation by directing other KSs to look for specific data Merpretation knowledge: All heuristic knowledge about transforming information on one level of the CBH to another level is represented as sets of production rules The rules in a KS usually generate inferences between adjacent levels However, some of the most powerful KSs generate inferences spanning several levels For example, a line with particular characteristics may immediately suggest a vessel class. This type of knowledge is very situation-specific. It was elicited from human experts who know and use much of the specialized, detailed knowledge now in the program (It is said that chess masters can immediately recognize approximately fifty thousand board patterns.) There are more examples of rules in the next section. The execution cycle consists of 1 focussing attention on pending time-dependent activities, on verification of a hypothesis, or on one of the hypothesized elements; 2 selecting the appropriate KSs for the attended event;

Abstract: An Expert System 10 for providing diagnostics to a data communications network 5. Alarms from a Network Manager 24 are received and queued by an Event Manager 117 and then filtered by an Alarm Filter 118 to remove redundant alarms. Alarms which are ready for processing are then posted to a queue referred to as a Bulletin Board 120. A Controller 112 determines which one of the posted goals has the highest priority by considering a priority number associated with the goal plus a time of arrival of the goal. An Inference Engine 122 uses information from an Expert Information Structure 111 to solve the highest priority goal by a process called instantiation. The process of solving the goal may be interrupted by a pause or suspension in order to perform tests under the direction of a Network Test Manager 124 or retrieve other information during which time other goals may be processed. Expert information is entered using a user friendly User Interface 104 which reduces need for the participation of a Knowledge Engineer. Configuration information about the network is maintained in a Network Structure Knowledge Base 109 by a Network Configuration Module 108. The Expert System 10 may operate in any of three modes: manual, wherein tests must be approved by or directed by an operator; automatic, where the tests are run automatically without operator intervention; and semiautomatic, where operator approval is required for certain tests such as interruptive tests and other tests such as non-interruptive tests may proceed without operator intervention.

Abstract: An autonomous expert system for directly maintaining remote computer systems by directly accessing the remote computer systems, diagnosing, and clearing fault conditions on those computer systems. The expert system performs those functions by first accessing a fault report from a centralized service reporting center, establishing a data connection to the computer system reporting the fault, invoking diagnostic routines on the computer system to gather data about the reported fault, analyzing the data, and, if appropriate, clearing the reported fault from the computer system. If the fault cannot be cleared, the expert system recommends maintenance procedures and replacement parts for a technician who the expert system dispatches to the remote computer. The recommendations are based on field experience stored in rules and databases maintained by the expert system. When the remote computer system is controlling a customr switching system (PBX), the expert system only invokes testing procedures in the computer system which do not disrupt stable telephone calls. The expert system access the PBX via the public telephone network.

Abstract: The authors address a complex, two-party negotiation problem containing the following elements: (1) many negotiation issues that are elements of a negotiating party's position; (2) negotiation goals that can be reduced to unequivocal statements about the problem domain and that represent negotiation issues; (3) a fluid negotiating environment characterized by changing issues and relations between them; and (4) parties negotiating to achieve goals that may change. They describe in some detail the way they logically specify different aspects of negotiation. An application of Negoplan to a labor contract negotiation between the Canadian Paperworkers Union and CIP, Ltd. of Montreal is described. >

Abstract: When I compare the books on expert systems in my library with the production expert systems I know of, I note that there are few good books on building expert systems in Prolog. Of course, the set of actual production systems is a little small for a valid statistical sample, at least at the time and place of this writing - here in Gennany, and in the first days of 1989. But there are at least some systems I have seen running in real life commercial and industrial environments, and not only at trade shows. I can observe the most impressive one in my immediate neighborhood. It is installed in the Telephone Shop of the Gennan Federal PTT near the Munich National Theater, and helps configure telephone systems and small PBXs for mostly private customers. It has a neat, graphical interface, and constructs and prices an individual telephone installation interactively before the very eyes of the customer. The hidden features of the system are even more impressive. It is part of an expert system network with a distributed knowledge base that will grow to about 150 installations in every Telephone Shop throughout Gennany. Each of them can be updated individually overnight via Teletex to present special offers or to adapt the selection process to the hardware supplies currently available at the local ware houses."

Abstract: A computer-aided system and method is disclosed for designing an application specific integrated circuit (ASIC) whose intended function is implemented both by a hardware subsystem including hardware elements on the integrated circuit and by a software subsystem including a general purpose microprocessor also on the integrated circuit. The system also generates software instructions for use by the software subsystem. The system utilizes a knowledge based expert system, with a knowledge base extracted from expert ASIC designers, and thus makes it possible for ASIC's to be designed and provided quickly and economically by persons not having the highly specialized skill of an ASIC designer.

Abstract: This paper presents an expert system developed in turbo prolog to identify faulted sections and interpret protective apparatus operation in large interconnected power systems. The expert system presented here is capable of identifying bus faults, line fault sections, and fault sections in the common area of a specific bus and line. Also, the expert system identifies relays or breakers malfunctions. The expert system is then expanded to include real-time measurements of current and voltage phasors to classify the type of fault that the faulted section has experienced. Furthermore, when the faulted section is a transmission line, the expert system selects an appropriate fault location algorithm to compute the fault location in miles. This paper shows that the combination of numeric and data base algorithm is essential to many developments in expert system application in power systems. Evaluating the expert systems reported so far for fault diagnosis reveals that all of these schemes utilize only the data received from breaker and relay status. Consider the recent trend in digital protection, real-time phasor measurements would be available. To combine real-time phasor measurements with relay and breaker status, a hybrid expert system is required. A hybrid expert system combines numeric algorithms with data base algorithms in one scheme. This paper recognizes this feature in the expert system developed here. The expert system reported in this paper includes four stages. The first stage determines the faulted section of the power system and reports correct and incorrect breaker and relay operation.

Abstract: The interdisciplinary anthropological study of knowledge elicitation (ASKE) project is introduced. The choice of anthropological methodology for the project is discussed, and the relevance of anthropology to knowledge engineering in general is pointed out. Examples are given of pitfalls observed in the knowledge elicitation process, including problems related to both interviewing technique and conceptual approach. Some preliminary conclusions are drawn based on work to date. >

Abstract: Gertis—a prototype expert system—not only demonstrates the feasibility of applying the Dempster-Shafer-based reasoning model to diagnosing hierarchically related hypotheses, but also suggests ways to generate better explanations by using knowledge about the structure of the hypothesis space and knowledge about the intended effects of the rules.

Abstract: Microcomputers can host real-time control expert systems. As Hexscon shows, the key is combining the best of conventional and expert-systm controllers.

Abstract: ***e FACHGEBIET*** Mathematical Geology, Computer Applications, Artificial Intelligence, Urban Economics and Regional Economics ***INTERESSENTENGRUPPE*** Of interest to Urban and Regional planners, civil engineers, geographers; computer scientists; operations researchers; landscape architects; and advanced students in the above disciplines.- Level: Technical Book, Monograph ***URHEBER*** T.J. Kim, University of Illinois, Champaign, IL; L.L. Wiggins, Massachusetts Institute of Technology, Cambridge, MA; J.R. Wright, Purdue University, Lafayette, IN (Eds.) ***TITEL*** Expert Systems: Applications to Urban Planning ***BIBLIOGRAPHISCHE-ANGABEN*** 1990. XIV, 268 pp. 48 figs. Hardcover DM 78, - ISBN 3-540-97171-8 ***LANGTEXT*** While expert systems have become a popular topic in the computing, medical and engineering fields, the expert system is still a new technology in urban planning. This book introduces expert systems for problem solving in urban planning and describes the way in which heuristic knowledge and rules of thumb of expert planners can be represented through computer programs. The book presents practical applications of expert systems for solving many important urban planning problems, particularly those issues that many practicing planners face in their daily operations. Problems and issues discussed are grouped in the following categories: - Land Use Planning - Transportation Planning - Site Selection and Analysis - Environmental Planning - Conflict Mediation and Legal Disputes - Future Developments and Directions Expert Systems: Applications to Urban Planning will benefit both urban planners who wish to learn how this new technology might be applied to their daily work as well as researchers in expert systems seeking new ideas for systems design.