Abstract: From the Publisher: In Artificial Intelligence, it is often said that the representation of knowledge is the key to the design of robust intelligent systems. In one form or another the principles of Knowledge Representation are fundamental to work in natural language processing, computer vision, knowledge-based expert systems, and other areas. The papers reprinted in this volume have been collected to allow the reader with a general technical background in AI to explore the subtleties of this key subarea. These seminal articles, spanning a quarter-century of research, cover the most important ideas and developments in the representation field. The editors introduce each paper, discuss its relevance and context, and provide an extensive bibliography of other work. Readings in Knowledge Representation is intended to serve as a complete sourcebook for the study of this crucial subject.

Abstract: Case Study: MYCIN: Varieties of Problem Solving Strategies The Anatomy of a Knowledge Base Anatomy of An Inference Engine MYCIN Reconsidered Languages and Tools for Knowledge Systems A Sampler of Knowledge Systems and Their Architectures How Knowledge Systems are Developed Near Futures: Knowledge Engineering in the Next Five Years Large Scale Knowledge Systems Near Futures: Intelligent Job Aids Not So Near Futures: Research Topics Likely to Bear Fruit in 5 Years or More Not So Near Futures: Intelligent Tutoring Systems Not So Near Futures: Planning and Preparing for the Knowledge Systems Revolution Appendixes

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Abstract: This paper describes knowledge acquisition strategies developed in the course of handcrafting a diagnostic system and reports on their consequent implementation in MORE, an automated knowledge acquisition system. We describe MORE in some detail, focusing on its representation of domain knowledge, rule generation capabilities, and interviewing techniques. MORE's approach is shown to embody methods which may prove fruitful to the development of knowledge acquisition systems in other domains.

Abstract: The intended purpose of this paper is twofold: proposing a common basis for the modeling of uncertainty and imprecision, and discussing various kinds of approximate and plausible reasoning schemes in this framework. Together with probability, different kinds of uncertainty measures (credibility and plausibility functions in the sense of Shafer, possibility measures in the sense of Zadeh and the dual measures of necessity, Sugeno's g?-fuzzy measures) are introduced in a unified way. The modeling of imprecision in terms of possibility distribution is then presented, and related questions such as the measure of the uncertainty of fuzzy events, the probability and possibility qualification of statements, the concept of a degree of truth, and the truth qualification of propositions, are discussed at length. Deductive inference from premises weighted by different kinds of measures by uncertainty, or by truth-values in the framework of various multivalued logics, is fully investigated. Then, deductive inferences from imprecise or fuzzy premises are dealt with; patterns of reasoning where both uncertainty and imprecision are present are also addressed. The last section is devoted to the combination of uncertain or imprecise pieces of information given by different sources. On the whole, this paper is a tentative survey of quantitative approaches in the modeling of uncertainty and imprecision including recent theoretical proposals as well as more empirical techniques such as the ones developed in expert systems such as MYCIN or PROSPECTOR, the management of uncertainty and imprecision in reasoning patterns being a key issue in artificial intelligence.

Abstract: The complexity of VLSI circuits has increased the need for design for testability (DFT). Numerous techniques for designing more easily tested circuits have evolved over the years, with particular emphasis on built-in testing approaches. What has not evolved is a design methodology for evaluating and making choices among the numerous existing approaches. This article describes efforts to build a knowledge-based expert system for designing testable VLSI chips. A framework for a methodology incorporating structural, behavioral, qualitative, and quantitative aspects of known DFT techniques is introduced. This methodology provides a designer with a systematic DFT synthesis approach. The process of partitioning a design into subcircuits for individual processing is described and a new concept?I-path?is used to transfer data from one place in the circult to another. Rules for applying testable design methodologies to circuit partitions and for evaluating the various solutions obtained are also presented. Finally, a case study using a prototype system is described.

Abstract: Retrieving problem-solving information from a human expert is a major problem when building an expert system. Methods from George Kelly's personal construct psychology have been incorporated into a computer program, the Expertise Transfer System, which interviews experts, and helps them construct, analyse, test and refine knowledge bases. Conflicts in the problem-solving methods of the expert may be enumerated and explored, and knowledge bases from several experts may be combined into one consultation system. Fast (one to two hour) expert system prototyping is possible with the use of the system, and knowledge bases may be constructed for various expert system tools.

Abstract: A tool for building a knowledge system and running a consultation on a computer is easily mastered by people with little computer experience yet also provides advanced capabilities for the experienced knowledge engineer. The knowledge system includes a knowledge base in an easily understood English-like language expressing facts, rules, and meta-facts for specifying how the rules are to be applied to solve a specific problem. The tool includes interactive knowledge base debugging, question generation, legal response checking, explanation, certainty factors, and the use of variables. The knowledge base language permits recursion and is extensible. Preferably, control during a consultation is goal directed in depth-first fashion as specified by rule order. The tool is easily embodied in assembly language, or in PROLOG to allow user-defined PROLOG functions.

Abstract: Principled development techniques could greatly enhance the understandability of expert systems for both users and system developers. Current systems have limited explanatory capabilities and present maintenance problems because of a failure to explicitly represent the knowledge and reasoning that went into their design. This paper describes a paradigm for constructing expert systems which attempts to identify that tacit knowledge, provide means for capturing it in the knowledge bases of expert systems, and, apply it towards more perspicuous machine-generated explanations and more consistent and maintainable system organization.

Abstract: Artificial intelligence and expert systems are the latest buzzwords and the hottest topics in the scientific community today Some experts are proclaiming that artificial intelligence (AI) has alrea...

Abstract: This article discusses the selection of the domain for a knowledge-based expert system for a corporate application. The selection of the domain is a critical task in an expert system development. At the start of a project looking into the development of an expert system, the knowledge engineering project team must investigate one or several possible expert system domains. They must decide whether the selected application(s) are best suited to solution by present expert system technology, or if there might be a better way (or, possibly, no way) to attack the problems. If there are several possibilities, the team must also rank the potential applications and select the best available. To evaluate the potential of possible application domains, it has proved very useful to have a set of desired attributes for good expert domain. This article presents such a set of attributes. The attribute set was developed as part of a major expert system development project at GTE Laboratories. It was used recurrently (and modified and expanded continually) throughout an extensive application domain evaluation and selection process.

Abstract: This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.

Abstract: The paper argues that user models are an essential component of any system which attempts to be “user friendly”, and that expert systems should tailor explanations to their users, be they super-experts or novices. In particular, this paper discusses a data-driven user modelling front-end subsystem, UMFE, which assumes that the user has asked a question of the main system (e.g. an expert system, intelligent tutoring system etc.), and that the system provides a response which is passed to UMFE. UMFE determines the user's level of sophistication by asking as few questions as possible, and then presents a response in terms of concepts which UMFE believes the user understands. Investigator-defined inference rules are then used to suggest additional concepts the user may/may not know, given the concepts the user indicated he or she knew in earlier questioning. Several techniques are discussed for detecting and removing inconsistencies in the user model. Additionally, UMFE modifies its inference rules for individual users when it detects certain types of inconsistencies. UMFE is a portable domain-independent implementation of a system which infers overlay models for users. UMFE has been used in conjunction with NEOMYCIN; and the paper contains several protocols which demonstrate its principal features. The paper concludes with a critique of UMFE and suggestions for enhancing the current system.

Abstract: Expert system projects are often based on collaboration with single domain expert. This leads to difficulties in judging the suitability of the chosen task and in acquiring the detailed knowledge required to carry out the task. This anecdotal article considers some of the advantages of using a diverse collection of domain experts.

Abstract: Abstract We present a VLSI implementation of an inference mechanism to cope with uncertainty and to perform approximate reasoning. The design is based on the “max-min operation” of fuzzy set theory for effective and real-time use. This inference mechanism can handle imprecise and uncertain knowledge; therefore, it can obtain human expert knowledge and simulate reasoning processes. An inference mechanism has been realized by using custom CMOS technology which emphasizes simplicity, extensibility, and efficiency. Timing simulation suggests that the inference engine can perform approximately 80,000 fuzzy logical inferences per second. A potential application of such inference engines is real-time decision making in the area of command and control and adaptive command generation of robotic systems.

Abstract: This paper describes ROGET, a knowledge-based system that assists a domain expert with an important design task encountered during the early phases of expert-system construction. ROGET conducts a dialogue with the expert to acquire the expert system's conceptual structure, a representation of the kinds of domain-specific inferences that the consultant will perform and the facts that will support these inferences. ROGET guides this dialogue on the basis of a set of advice and evidence categories. These abstract categories are domain independent and can be employed to guide initial knowledge acquisition dialogues with experts for new applications. This paper discusses the nature of an expert system's conceptual structure and describes the organization and operation of the ROGET system that supports the acquisition of conceptual structures.

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Abstract: SOCRATES is a rule-based expert system that optimizes combinational logic for a specific target technology. The system performs substitutions of equivalent gate configurations, thereby reducing the overall area of the implementation and improving the speed of the design. A control mechanism uses various backup strategies to choose the rules applied to the circuit. Users can easily extend the library of transformation rules through a rule generation module that automatically encodes rules and inserts them into the knowledge base. Timing constraints placed on the circuit can be modified to allow the designer to explore a large design space in a matter of minutes. Implementations generated by the system are comparable in area and speed to circuits designed by experts.

Abstract: A method of integrating an expert system having a knowledge base of elevator trouble-shooting information into the working environment of elevator service personnel, without special training of such personnel, and without compromising the security of the knowledge base. The method includes an interactive initialization procedure which includes successive, successful user and knowledge base initiated communication links between the user and knowledge base, before actual access to the knowledge base is permitted.

Abstract: Existing expert systems have a high percentage agreement with experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes, and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the integrated diagnostic model (IDM), that integrates two sources of knowledge, a shallow, reasoning-oriented, experiential knowledge base and a deep, functionally oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair, an implementation in the mechanical domain is described.

Abstract: We show how Shafer's concepts of plausibility and belief can be derived as a special case of the compatibility of a linguistically quantified statement with a data base consisting of an expert's fragmented opinion as to the location of a special element. We also show that probability is the compatibility of a linguistically quantified statement about truth with this data base. Based upon these ideas we provide some alternative methods to Dempster's rule for combining evidence.

Abstract: In structural engineering practice, situations exist where the available information is inexact or imprecise. Frequently, experienced structural engineers are capable of providing meaningful answers to such problems. The purpose of this investigation is to construct an expert system called SPERIL-II for the damage assessment of existing structures on the basis of the knowledge of experienced structural engineers. SPERIL-II is a knowledge-based damage assessment system in which there are the following three steps in the assessment process: ( 1 ) the evaluation of local damageability from input data, (2) the evaluation of global damageability, (3) the estimation of the safety or damage state of the structure. This paper introduces an inexact inference using AND/OR/COMB relation and Dempster's rule of combination to combine two fuzzy sets with certainty factors. This inexact inference is used in all steps, and a suitable measure is given according to the importance of the structure in step (3).

Abstract: Several different uncertain inference systems (UISs) have been developed for representing uncertainty in rule-based expert systems. Some of these, such as Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as approximations to probability, and others, such as Fuzzy Set Theory and Dempster-Shafer Belief Functions were not. How different are these UISs in practice, and does it matter which you use? When combining and propagating uncertain information, each UIS must, at least by implication, make certain assumptions about correlations not explicily specified. The maximum entropy principle with minimum cross-entropy updating, provides a way of making assumptions about the missing specification that minimizes the additional information assumed, and thus offers a standard against which the other UISs can be compared. We describe a framework for the experimental comparison of the performance of different UISs, and provide some illustrative results.

Abstract: From the Publisher: VLSI design synthesis is a method for designing hardware that starts an algorithmic description and uses interactive computer programs to a finished design. This structured approach can decrease the time takes to design a chip, automatically provide multi-level documentation the finished design, and create reliable and testable designs. VLSI synthesis is a difficult problem because the huge number of facts implicit dynamic constraints do not lend themselves to a recipe-like solution. However, the Knowledge Based Expert System, KBES, approach provides a framework for organizing solutions to problems that are currrently solved by experts using large amounts of domain-specific knowledge.