Abstract: Research in discourse analysis, story understanding, and user modeling for expert systems has shown great interest in plan recognition problems. In a plan recognition problem, one is given a fragmented description of actions performed by one or more agents, and expected to infer the overall plan or scenario which explains those actions. This thesis develops the first formal description of the plan recognition process. Beginning with a reified logic of events, the thesis presents a scheme for hierarchically structuring a library of event types. A semantic basis for non-deductive inference, called "minimum covering entailment", justifies the conclusions that one may draw from a set of observed actions. Minimum covering entailment is defined by delineating the class of models in which the library is complete and the set of unrelated observations is minimized. An equivalent proof theory forms a preliminary basis for mechanizing the theory. Equivalence theorems between the proof and model theories are presented. Minimum covering entailment is related to a formalism for non-monotonic inference known as "circumscription". Finally, the thesis describes a number of algorithms which correctly implement the theory, together with a discussion of their complexity. The theory is applied to a number of examples of plan recognition, in domains ranging from an operating system advisor to the theory of speech acts. The thesis shows how problems of medical diagnosis, a similar kind of non-deductive reasoning, can be cast in the framework, and an example previously solved by a medical expert system is worked out in detail. The analyses provides a firm theoretical foundation for much of what is loosely called "frame based inference", and directly accounts for problems of ambiguity, abstraction, and complex temporal interactions, which were ignored by previous work. The framework can be extended to handle difficult phenomena such as errors, and can also be restricted in order to improve its computational properties in specialized domains.

Abstract: The first step in the development of an expert system is the extraction and characterization of the knowledge and skills of an expert. This step is widely regarded as the major bottleneck in the system development process. To assist knowledge engineers and others who might be interested in the development of an expert system, I offer (1) a working classification of methods for extracting an expert's knowledge, (2) some ideas about the types of data that the methods yield, and (3) a set of criteria by which the methods can be compared relative to the needs of the system developer. The discussion highlights certain issues, including the contrast between the empirical approach taken by experimental psychologists and the formalism-oriented approach that is generally taken by cognitive scientists.

Abstract: THE MOST EFFECTIVE METHOD OF IMPROVING THE RETRIEVAL PERFORMANCE OF A DOCUMENT RETRIEVAL SYSTEM IS TO ACQUIRE A DETAILED SPECIFICATION OF THE USER''S INFORMATION NEED. THE SYSTEM DESCRIBED IN THIS PAPER, (I(EXPONENT 3)R), PROVIDES A NUMBER OF FACILITIES AND SEARCH STRATEGIES BASED ON THIS APPROACH. THE SYSTEM USES A NOVEL ARCHITECTURE TO ALLOW MORE THAN ONE SYSTEM FACILITY TO BE USED AT A GIVEN STAGE OF A SEARCH SESSION. USERS INFLUENCE THE SYSTEM ACTIONS BY STATING GOALS THEY WISH TO ACHIEVE, BY EVALUATING SYSTEM OUTPUT, AND BY CHOOSING PARTICULAR FACILITIES DIRECT- LY. THE OTHER MAIN FEATURES OF (I(EXPONENT 3)R)) ARE AN EMPHASIS ON DOMAIN KNOWLEDGE USED FOR REFINING THE MODEL OF THE INFORMATION NEED, AND THE PROVISION OF A BROWSING MECHANISM THAT ALLOWS THE USER TO NAVIGATE THROUGH THE KNOWLEDGE BASE.

Abstract: An expert system which provides one or more suggested treatments for a patient with physical trauma is disclosed. The system includes a computing device having a memory, a plurality of data bases in the memory, an application program and an inference engine program. The data bases include graphical illustrations of different types of physical trauma, and a knowledge base which contains treatment information. The application program is executed in the computing device and interactively displays a series of screens including at least some of the graphical illustrations, to elicit responses from the user concerning the specific types of physical trauma and specific characteristics of the patient. The inference engine program, which is also executed in the computing device, uses the knowledge base and information related to the responses elicited from the user, for selecting one or more suggested treatments. The application program presents the suggested treatments to the user after execution of the inference engine program.

Abstract: The issue of how to effectively integrate and use symbolic causal knowledge with numeric estimates of probabilities in abductive diagnostic expert systems is examined. In particular, a formal probabilistic causal model that integrates Bayesian classification with a domain-independent artificial intelligence model of diagnostic problem solving (parsimonious covering theory) is developed. Through a careful analysis, it is shown that the causal relationships in a general diagnostic domain can be used to remove the barriers to applying Bayesian classification effectively (large number of probabilities required as part of the knowledge base, certain unrealistic independence assumptions, the explosion of diagnostic hypotheses that occurs when multiple disorders can occur simultaneously, etc.). Further, this analysis provides insight into which notions of "parsimony" may be relevant in a given application area. In a companion paper, Part Two, a computationally efficient diagnostic strategy based on the probabilistic causal model discussed in this paper is developed.

Abstract: Fault diagnosis is the problem of determining the root causes of process upsets. This paper presents a very efficient method of identifying the possible causes of process disturbances using the signed directed graph (digraph) representation of process interactions. The analysis is based on forming logical statements (rules) derived from the process digraph; these are evaluated using on-line data to yield the diagnosis. Evaluation of rule antecedents is more efficient than the previous algorithmic approach of Shiozaki et al. In the rule-based approach, the diagnostic criteria are represented explicitly, not hidden by a complex algorithmic procedure. This allows the diagnostic rules to be tailored to reflect the best available knowledge of plant behavior. The rules generated by this technique can be integrated with other rules on plant operations using an expert systems framework.

Abstract: Distributed problem-solving is defined as a subfield of artificial intelligence that deals with the interaction of groups of intelligent agents attempting to cooperate to solve problems. A taxonomy of distributed artificial intelligence systems is presented, based on the communication and control methodologies used by their constituent agents, along with the theoretical foundations which underly them. Control in distributed problem-solvers is characterized by cooperation, organization, and dynamics. Communications are specified through paradigms, content, and protocols. Several prototypical systems in areas such as natural language processing and medical diagnosis are briefly discussed, along with more mature systems in applications such as air-traffic control, vehicle monitoring, and manufacturing systems.

Abstract: Arguments are adduced to support the claim that the only satis- factory description of uncertainty is probability. Probability is described both mathematically and interpretatively as a degree of belief. The axio- matic basis and the use of scoring rules in developing coherence are discussed. A challenge is made that anything that can be done by alternative methods for handling uncertainty can be done better by probability. This is demonstrated by some examples using fuzzy logic and belief functions. The paper concludes with a forensic example illustrating the power of probability ideas.

Abstract: Expert systems are now recognized as key elements in the computing systems of the future, and the most significant applications of AI. Expert systems are already being used in such varied fields as medicine, engineering, chemistry and business.

Abstract: Read more and get great! That's what the book enPDFd structured induction in expert systems will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this structured induction in expert systems, what you will obtain is something great.

Abstract: Current expert system technology tends to rely on the use of shallow empirically based experiential knowledge. With only this type of knowledge available, expert systems have been capable of reaching a high level of agreement with human experts in a limited area of expertise. However, due to the nature of their knowledge, such systems fall short of human expertise in many ways. The human diagnostic process is examined as it relates to the malfunction of mechanical and electrical devices. An expert system design is presented, called the integrated diagnostic model (IDM), that attempts to address some of the issues involved in bridging the gap between human and computer expertise. The IDM contains two different types of knowledge, one based on experience and one based on how the device to be diagnosed functions. These two types of knowledge are used together during a diagnostic session to determine what is wrong with the device. To demonstrate how the IDM works, an interaction with a prototype system that was built using the IDM is described; then research on extensions to the IDM is discussed.

Abstract: This paper describes behavioral compilation tools built for use in an intelligent silicon compiler. These tools allow the user or an expert system to compile behavioral descriptions to a register transfer level under user-imposed constraints. A flexible design model offers a combination of design features previously unavailable in behavioral compilers such as multicycle, chained, and pipelined function units along with the ability to choose between bus- and mux-based connectivity models. Furthermore, we present a new design strategy that allows easy exploratory design and we describe algorithms for state synthesis and connectivity binding that achieved higher quality designs than previous systems on selected benchmarks. The code for this project is run under 42 BSD Unix on a VAX 11 780 and is written in C.

Abstract: In this paper we look at the evolutionary development of Computer Assisted Instruction from the early days of ‘linear programs’ up to the use of ‘expert systems’ in education and training. We present the basic principles of Intelligent Tutoring Systems (ITS) which are capable of rich interaction with the student, which know how to teach, and who and what they are teaching. We point out the need for knowledge representation formalisms which can support ITS and present one such formalism (production systems). In the framework presented we describe systems developed for the teaching of modern languages, electronic trouble shooting and computer programming. Finally we point out the shortcomings of ITS and identify areas where a consensus of opinion does not exist. © 1986 Wiley Periodicals, Inc. (Masoud Yazdani: Masoud Yazdani studied Computer Science at the University of Essex and Artificial Intelligence at the University of Sussex. He joined Exeter University in 1980 as a lecturer in Computer Science. His research work is in two areas of computational models of creativity and intelligent tutoring systems. This latter area has led to implementation of various systems using artificial intelligence techniques to teach arithmetic and foreign languages. He is Chairman of Intellect Limited, a company specialising in applications of AI and to educate industry in what AI can offer it. He has acted as a consultant to various industrial companies including Acorn Computers, GTE Inc. and ITT Engineering Support Centre. He is the Committee Secretary for the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB). His books include New Horizons in Educational Computing, Artificial Intelligence: Human Effects, both published by Ellis Horwood Ltd. and Artificial Intelligence: Principles and Applications published by Chapman and Hall.)

Abstract: This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer/end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria.

Abstract: This paper proposes a set of definitions for the concepts "validation" and "assessment" applied to expert systems (ESs). It develops a framework for this validation and demonstrates the framework on existing accounting and auditing ESs to elicit some of the research issues involved in ES validation. Validation is critical to the design and implementation of decision-making ESs. In a setting where objectivity is sought and variance is avoided, validation ascertains what a system knows, knows incorrectly. or does not know. Validation ascertains the system's level of exper­ tise and investigates the theoretical basis on which the system is based. It evaluates the reliabili­ ty of decisions made by the system. The validation framework developed in this paper is research methods. It is designed to reflect the unique aspects of ESs (in contrast to other types of computer programs) and can be used by ES developers as a basis from which to perform validation and by researchers as a framework to elicit research issues in validation. Subject Areas: Accounting Theory and Auditing.

Abstract: Historically, the study of artificial intelligence has emphasized symbolic rather than numerical computation. In recent years, however, the practical needs of expert systems have led to an interest in the use of numbers to encode partial confidence. There has been some effort to square the use of these numbers with Bayesian probability ideas, but in most applications not all the inputs required by Bayesian probability analyses are available. This difficulty has led to widespread interest in belief functions, which use probability in a looser way. It must be recognized, however, that even belief functions require more structure than is provided by pure production systems. The need for such structure is inherent in the nature of probability argument and cannot be evaded. Probability argument requires design as well as numerical inputs. The real challenge probability poses to artificial intelligence is to build systems that can design probability arguments. The real challenge artificial intelligence poses to statistics is to explain how statisticians design probability arguments.

Abstract: This paper gives a state-of-the-art report on the use of techniques based on personal construct psychology to automate knowledge engineering for expert systems. It presents the concept of knowledge support systems as interactive knowledge engineering tools, states the design criteria for such systems, and outlines the structure and key components of the KITTEN implementation. KITTEN includes tools for interactive repertory grid elicitation and entailment analysis that have been widely used for rapid prototyping of industrial expert systems. It also includes tools for text analysis, behavioral analysis and schema analysis, that offer complementary and alternative approaches to knowledge acquisition. The KITTEN implementation integrates these tools around a common database with utilities designed to give multiple perspectives on the knowledge base.

Abstract: Over the past 6 years we have been developing a computer program to teach medical diagnosis. Our research synthesizes and extends results in artlficlal intelligence (Al), medicine, and cognitive psychology. This paper describes the progression of the research, and explalns how theories from these fields are combined in a computational model. The general problem has been to develop an "intelligent tutoring system" by adapting the MYCIN "expert system." Thls conversion requires a deeper understanding of the nature of expertise and explanatlon than origlnally requlred for developlng MYCIN, and a concomitant shift in perspective from slmple performance goals to attaining psychologlcal validity in the program''s reasoning process. Others have written extensively about the relatlon of artificlal intelligence to cognltive sclence (e.g., [Pylyshyn, 1978] [Boden, 1977]). Our purpose here is not to repeat those arguments, but to present a case study which will provide a common point for further dlscusslon. To this end, to help evaluate the state of cognitive science, we will outline our methodology and survey what resources and viewpoints have helped our research. We will also discuss pitfalls that other Al-oriented cognitive scientists may encounter. Finally, we will present some questions coming out of our work whlch might suggest possible collaboration with other fields of research.

Abstract: An approach to chemical plant fault diagnosis is presented that utilizes patterns of violation and satisfaction of the quantitative constraints governing the process. Process knowledge consists of a list of the operational constraints on the plant together with sufficient conditions for violation of each constraint. Interpretation of the pattern of constraint violations is treated by Boolean and non-Boolean techniques. It is shown that non-Boolean reasoning techniques increase the stability and sensitivity of the diagnosis in the presence of noise. The techniques introduced in this paper are easily implemented in rule-based expert systems using certainty factors.

Abstract: This memorandum reviews the factors that constitute an Expert System Building Tool (ESBT) and evaluates current tools in terms of these factors. Evaluation of these tools is based on their structure and their alternative forms of knowledge representation, inference mechanisms and developer end-user interfaces. Next, functional capabilities, such as diagnosis and design, are related to alternative forms of mechanization. The characteristics and capabilities of existing commercial tools are then reviewed in terms of these criteria.

Abstract: The aim of the Expert Systems in Computer-Aided Design conference was to provide a forum for the exchange of ideas and experiences related to expert systems in computer-aided design, to present and explore the state-of-the-art of expert systems in computer-aided design, to delineate future directions in both research and practice and to promote further development. Seventeen of the nineteen papers accepted were presented with each presentation followed by a round table discussion. The discussion was taped, transcribed and edited and forms part of this volume. The authors came from seven countries, whilst the attendees represented some thirteen nationalities. There is an implicit structure in the ordering of the papers, commencing with system architectures, representation tools through applications to specific design concerns. These papers demonstrate the wide variety of knowledge engineering tools needed in computer-aided design. It is interesting to observe the progression over these three conferences in the ratio of computer scientists to design researchers amongst the authors. The balance over the period has swung from a predominance of computer scientists to a predominance of design researchers. We are beginning to see knowledge engineering development driven by designers' needs

Abstract: An expert system shell efficiently computes functions of variables in response to numeric or symbolic data values input by a user. The system comprises a Knowledge Base in the form of a network of functions, an Inference Engine for efficiently updating values in the knowledge base in response to changes in entered data, and a Forms System that manages interaction with the user. A knowledge engineer creates the network of functions, and defines the user screens and the connection between screen objects and variables in the function network. The system allows many different types of variables, including numeric and symbolic types. The system associates a probability distribution with every variable, and computes the probability distributions for the dependent variables from the probability distributions for the independent variables. A variable can store multiple values as tables of probability distributions keyed by one or more key variables. When a user action changes the probability distributions for any variable, the system automatically maintains the specified functional relationships among all the related variables.

Abstract: A hybrid system for automatic knowledge acquisition for expert systems is presented. The system integrates artificial intelligence and cognitive science methods to construct knowledge bases employing different knowledge representation formalisms. For the elicitation of human declarative knowledge, the tool contains automated interview methods. The acquisition of human procedural knowledge is achieved by protocol analysis techniques. Textbook knowledge is captured by incremental text analysis. The goal structure of the knowledge elicitation methods is an intermediate knowledge-representation language on which frame, rule and constraint generators operate to build up the final knowledge bases. The intermediate knowledge representation level regulates and restricts the employment of the knowledge elicitation methods. Incomplete knowledge is laid open by patterndirected invocation methods (the intermediate knowledge base watcher) triggering the elicitation methods to supplement the necessary knowledge.

Abstract: “When the expert entered the room, everyone knew it. There was a kind of aura which surrounded him.”