Showing papers on "Domain knowledge published in 1984"

The Knowledge-Based Expert System: A Tutorial

Abstract: Knowledge-based expert systems, or knowledge systems for short, employ human knowledge to solve problems that ordinarily require human intelligence.1 Knowledge systems represent and apply knowledge electronically. In the future, these capabilities will ultimately make knowledge systems vastly more powerful than the earlier technologies for storing and transmitting knowledge, books and conventional programs. These earlier storage and transmission technologies suffer from fundamental limitations. Although books now store the largest volume of knowledge, they merely retain symbols in a passive form. Before the knowledge stored in books can be applied , a human must retrieve it, interpret it, and decide how to exploit it for problem-solving. Most computers today perform tasks according to the decision-making logic of conventional programs, but these programs do not readily accommodate significant amounts of knowledge. Programs consist of two distinct parts, algorithms and data. Algorithms determine how to solve specific kinds of problems, and data characterize parameters in the particular problem at hand. Human knowledge doesn't fit this model, however. Because much human knowledge consists of elementary fragments of know-how, applying a significant amount of knowledge requires new ways to organize decision-making fragments into useful entities. Knowledge systems collect these fragments in a knowledge base, then access the knowledge base to reason about specific problems. As a consequence, knowledge systems differ from conventional programs in the way they are organized, the way they incorporate knowledge, the way they execute, and the impression they create through their interactions. Knowledge systems simulate expert human performance and present a human-like facade to the user. * Advising about computer system use, and * VLSI design. In all of these areas, system developers have worked to combine the general techniques of knowledge engineering with specialized know-how in particular domains of application. In nearly every case, the demand for a knowledge engineering approach arose from the limitations perceived in the alternative technologies available. The developers wanted to incorporate a large amount of fragmentary, judgmental, and heuristic knowledge; they wanted to solve automatically problems that required the machine to follow whatever lines of reasoning seemed most appropriate to the data at hand; they wanted the systems to accommodate new knowledge as it evolved; and they wanted the systems to use their knowledge to give meaningful explanations of their behaviors when requested. This article presents an overview of the field of knowledge engineering. It describes the major developments that have led up to the current …

Knowledge and common knowledge in a distributed environment

Abstract: We argue that the right way to understand distributed protocols is by considering how messages change the state of knowledge of a system. We present a hierarchy of knowledge states that a system may be in, and discuss how communication can move the system's state of knowledge of a fact up the hierarchy. Of special interest is the notion of common knowledge. Common knowledge is an essential state of knowledge for reaching agreements and coordinating action. We show that in practical distributed systems, common knowledge is not attainable. We introduce various relaxations of common knowledge that are attainable in many cases of interest. We describe in what sense these notions are appropriate, and discuss their relationship to each other. We conclude with a discussion of the role of knowledge in distributed systems.

Knowledge engineering: the applied side of artificial intelligence

Abstract: : Expert System research in an emerging area of computer science that exploits the capabilities of computers for symbolic manipulation and inference to solve complex and difficult reasoning problems at the level of performance of human experts. The methods of this area are designed to acquire and represent both the formal and the informal knowledge that experts hold about the tasks of their discipline. Numerous applications to science, engineering, and medicine have been accomplished. Expert System projects represent applied artificial intelligence research, though they also make salient numerous fundamental research issues in the acquisition, representation and utilization of knowledge by computer programs. Knowledge engineering approaches promise significant cost savings in certain applications; intelligent computer-based aids for practitioners in fields whose knowledge is primarily nonmathematical; and the elucidation of the heuristic knowledge of experts -- the largely private knowledge of practice. There are major problems of knowledge engineering including the shortage of adequate computer equipment, the shortage of trained specialists in applied artificial intelligence, the scientific base for adequate knowledge acquisition, and the lack of sustained funding. (Author)

Knowledge-Based Expert Systems

Abstract: More than technological wonders, knowledge systems are valuable human assistants, equalling or surpassing experts in reasoning and judgment. Since human work consists mostly of knowledge-based reasoning, future needs should increase. For years it remained little more than a bold experiment, an avant-garde technology with matchless capabilities but with painfully restricted applications. Since 1981, however, the emerging field of expert and knowledge-based systems has changed dramatically. No longer the exclusive property of diagnosticians and a few other medical specialists, the systems are finally starting to broaden their user base and wriggle their way into mainstream, commercial applications. Like infants taking their first halting steps, expert and knowledge-based systems are slowly toddling out of basic research laboratories and making places for themselves in big business. systems, or \"knowledge systems\" for short, have evolved over a 15-year period from laboratory curiosities of applied artificial intelligence into targets of significant technological and commercial development efforts 3 These systems employ computers in ways that differ markedly from conventional data processing applications , and they open up many new opportunities. The people who build these new systems have adopted the title of \"knowledge engineer\" and call their work \"knowledge engineering.\" Recently, many commercial and governmental organizations have committed themselves to exploiting this technology, attempting to advance it in dramatic ways and beginning to adapt their missions and activities to it. A staggering number of events occurred during the last five years: * Schlumberger, a leading oil services firm, determined that its future growth depended on knowledge engineering and formed two groups to build expert data interpretation systems. * Japan's Ministry of International Trade and Industry determined that the country's future economic viability required leadership in knowledge system technology and launched a $500 million, 10-year program in fifth-generation computing .3 * Responding to a perceived technological and competitive threat, UK's Alvey Commission retracted that country's long-standing disapproval of Al and urged a major push forward in knowledge systems technology, a recommendation that the Thatch-er government implemented.

Expert systems: art alternative paradigm

Abstract: There has recently been a significant effort by the A.I. community to interest industry in the potential of expert systems. However, this has resulted in far fewer substantial applications projects than might be expected. This article argues that this is because human experts are rarely required to perform the role that computer-based experts are programmed to adopt. Instead of being called in to answer well-defined problems, they are more of ten asked to assist other experts to extend and refine their understanding of a problem area at the junction of their two domains of knowledge. This more properly involves educational rather than problem-solving skills. An alternative approach to expert system design is proposed based upon guided discovery learning. The user is provided with a supportive environment for a particular class of problem, the system predominantly acting as an advisor rather than directing the interaction. The environment includes a database of domain knowledge, a set of procedures for its application to a concrete problem, and an intelligent machine-based advisor to judge the user's effectiveness and advise on strategy. The procedures focus upon the use of user generated “explanations” both to promote the application of domain knowledge and to expose understanding difficulties. Simple database PROLOG is being used as the subject material for the prototype system which is known as MINDPAD.

On the Development of Commercial Expert Systems

Abstract: We use our experience with the Dipmeter Advisor system for well-log interpretation as a case study to examine the development of commercial expert system. We discuss the nature of these systems as we see them in the coming decade, characteristics of the evolution process, development methods, and skills required in the development team. We argue that the tools and ideas of rapid prototyping and successive refinement accelerate the development process. We note that different types of people are required at different stages of expert system development: Those who are primarily knowledgeable in the domain, but who can use the framework to expand the domain knowledge; and those who can actually design and build expert systems. Finally, we discuss the problem of technology transfer and compare our experience with some of the traditional wisdom of expert system development.

The Logic of Incomplete Knowledge Bases

Abstract: Some formal representation issues underlying the use of incomplete knowledge bases are discussed. An incomplete knowledge base is one that has only partial knowledge of the application domain. It is argued that a language that can refer to both the application domain and to the state of the knowledge base is required to specify and to question an incomplete knowledge base. A formal logical language with this expressive ability is presented and its semantics and proof theory are defined. It is also shown how different the use of the language must be, depending on whether the interaction involves querying or defining the knowledge base.

A Review of knowledge-based planning techniques

Abstract: Planning systems have been an active research topic within Artificial Intelligence for over two decades. There have been a number of techniques developed during that period which still form an essential part of many of today's planners. This paper introduces the techniques, attempts to classify some of the important research themes in AI planning and describes their historical development.

A Model-Theoretic Analysis Of Knowledge: Preliminary Report

Abstract: Understanding knowledge is a fundamental issue in many disciplines. In computer science, knowledge arises not only in the obvious contexts (such as knowledge-based systems), but also in distributed systems (where the goal is to have each processor "know" something, as in Byzantine agreement). A general semantic model of knowledge is introduced, to allow reasoning about statements such as "He knows that I know whether or not she knows whether or not it is raining." This approach more naturally models a state of knowledge than previous proposals (including Kripke structures). Using this notion of model, a model theory for knowledge is developed. This theory enables one to interpret such notions as a "finite amount of information" and "common knowledge" in different contexts.

An Experimental Comparison of Knowledge Representation Schemes

Abstract: Many techniques for representing knowledge have been proposed, but there have been few reports that compare their application. This article presents an experimental comparison of four knowledge representation schemes: a simple production system, a structured production system. A frame system, and a logic system. We built four pilot expert systems to solve the same problem: risk management of a large construction project. Observations are made about hoe the structure of the domain knowledge affects the implementation of expert systems and their run time efficiency.

Logical Levels of Problem Solving.

Abstract: This paper demonstrates how clear, efficient problem solving programs can be written within logic programming The key point is the consideration of levels involved, both in the problem solving itself and in the underlying logic Three levels of knowledge necessary for intelligent problem solving are identified—a level of domain knowledge, a level of methods and strategies, and a planning level The approach introduced here relates these levels to the distinction between object and meta languages Two classes of programs are presented First, single level problem solvers are introduced These are at the methods level and constitute a meta language of the problem domain Second, flexible multilevel problem solvers are outlined which can be built as extensions of the single level programs

Logical levels of problem solving

Abstract: This paper demonstrates how clear, efficient problem solving programs can be written within logic programming. The key point is the consideration of levels involved, both in the problem solving itself and in the underlying logic. Three levels of knowledge necessary for intelligent problem solving are identified—a level of domain knowledge, a level of methods and strategies, and a planning level. The approach introduced here relates these levels to the distinction between object and meta languages. Two classes of programs are presented. First, single level problem solvers are introduced. These are at the methods level and constitute a meta language of the problem domain. Second, flexible multilevel problem solvers are outlined which can be built as extensions of the single level programs.

A short account of Knowledge Engineering

Abstract: Although expert systems and knowledge engineering are attracting great attention there is still confusion about what they are. Most expert systems deal with problems which are familiar to other disciplines and their distinctive character is not always recognised. This tutorial describes the origins of expert systems in Artificial Intelligence, and the technical concept of “knowledge”. The central feature is the ideal of explicitly representing knowledge as facts, rules and other symbolic structures, rather than the traditional representation as abstract numbers or algorithms. These developments yield new solutions to old problems, and ways of solving problems whcih have previously been thought to be the preserve of the human intellect.

Database Access Requirements of Knowledge-Based Systems

Abstract: Knowledge bases constitute the core of those Artificial Intelligenceprograms which have come to be known as Expert Systems. Anexamination of the most dominant knowledge representation schemes usedin these systems reveals that a knowledge base can, and possiblyshould, be described at several levels using different schemes,including those traditionally used in operational databases. Thischapter provides evidence that solutions to the organization andaccess problem for very large knowledge bases require the employmentof appropriate database management methods, at least for the lowestlevel of description -- the facts or data. We identify the databaseaccess requirements of knowledge-based or expert systems and thenpresent four general architectural strategies for the design of expertsystems that interact with databases, together with specificrecommendations for their suitability in particular situations. Animplementation of the most advanced and ambitious of these strategiesis then discussed in some detail.

The Representation of Knowledge: Curricular and Instructional Implications for Science Teaching

Abstract: Publisher Summary This chapter discusses techniques for knowledge presentation in the context of science curriculum development. The techniques fall into two categories: (1) those that can be used to represent conceptual knowledge and (2) those that can be used to represent procedural knowledge (flowcharts). These techniques can assist the curriculum planner in surveying disciplinary knowledge, selecting curricular content, and sequencing the selected content. The knowledge representations serve a single purpose to assist the curriculum developer and teacher to better organize the conceptual knowledge in a discipline. If representations are seen as tools to sharpen the curriculum developer's knowledge of a discipline's conceptual organization and the interrelation of the conceptual with the procedural, they can be valuable. If the coherence of the design is increased, the probability of increasing the clarity with which students organize such knowledge is also increased.

Signal-to-symbol transformation: Reasoning in the HASP/SIAP program

Abstract: In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based, or expert, systems. [4] They have shown how the use of task domain knowledge combined with symbolic manipulation techniques can be of use in making signal understanding systems more effective and efficient. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the AI techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.

A general expert system design for diagnostic problem solving

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, as well as an example in the medical domain.

Interactive classification: a technique for building and maintaining knowledge bases

Abstract: The practical application of knowledge-based systems, such as in expert systems, often requires the maintenance of large amounts of declarative knowledge. As a knowledge base grows in size and complexity, it becomes more difficult to maintain and extend. Even someone who is familiar with the representation and the contents of the existing knowledge base may introduce inconsistencies and errors whenever an addition or modification is made. This paper describes an approach to this problem based on a tool called an interactive classifier. An interactive classifier uses the contents of the existing knowledge base and knowledge about its representation to help the maintainer describe new knowledge base objects. The interactive classifier will identify the appropriate taxononomic location for the newly described object and add it to thr knowledge base. The new object is allowed to be a generalization of existing knowledge base objects, enabling the system to learn more about existing obects.

Knowledge base systems in computer aided technology

Abstract: The evolution of artificial intelligence technology over the past decade has lead to the development of a mature application area known as knowledge based systems. Knowledge base systems are computer programs capable of reasoning based on information maintained in a domain specific knowledge base. This paper describes the requirements of knowledge base system construction and discusses their relevance in the area of computer aided technology. In particular, a potential implementation of a knowledge-based system capable of computer-aided design is discussed.

From Geological Knowledge to Computational Relationships: A Case Study of the Expertise of Programming

Abstract: The role of domain knowledge in the creation of scientific software is currently being investigated in the development of an automatic programming system for use by experts in the domain of quantitative log interpretation.1 The goal in this problem domain is the deduction of geological and petrophysical properties in the environment of an oil well from signals provided by measuring devices which are lowered into that well. The signal data are known as “well logs;” and the measuring devices are called “logging tools.”

Learning problem classes by means of experimentation and generalization

Abstract: We discuss a method of learning by practice based on the idea of determining classes of problems that can be solved in simplified ways. A description of a class is obtained by processes that hypothesize descriptions, generate and classify problem variations, and test the hypotheses against them. The approach has been implemented in a system that learns by practice in a domain of elementary physics. The system has two main components, a Problem Solver and a Learning Agent. The Problem Solver handles the problems in the domain and the Learning Agent does the actual learning. To perform its tasks the Learning Agent utilizes algorithms, heuristics, and domain knowledge, and for this reason it can be regarded as an expert system whose expertise resides in being able to learn by experimentation and generalization.

A framework for expert control of interactive software systems

Abstract: Expert problem-solving strategies in many domains require the use of detailed mathematical techniques coupled with experiential knowledge about how and when to use the appropriate techniques. In many of these domains, such techniques are made available to experts in large software packages. In attempting to build expert systems for these domains, we wish to make use of these existing packages, and are therefore faced with an important problem: how to integrate the existing software, and knowledge about its use, into a practical expert system. The expert knowledge is used, in dynamic selection of appropriate programs and parameters, to reach a successful goal in the problem-solving. This kind of expert problem-solving is achieved through two interacting bodies of knowledge; problem domain knowledge, and knowledge about the programs that comprise the software package. This thesis describes the framework of a hybrid expert system for representing problem-solving knowledge in these domains. This hybrid system may be characterized as consisting of a surface model and a deep model. The surface model is a production-rule based expert subsystem that consists of heuristics used by an expert. The deep model is a collection of methods, each parameterized by a set of controlling and observed parameters. The methods and their results are reasoned about using their parameter sets. The existing software is reorganized as necessary to map it into the deep model structure of a hybrid system. This framework has evolved out of an effort to build an expert system for performing well-log analysis (ELAS - Expert Log Analysis System). A generalized expert-system building methodology based upon principles drawn from ELAS is introduced. The use of method-abstractions in assembling a hybrid system is discussed. The notion of worksheet-reasoning is defined, and discussed.

Very-high-level programming of knowledge representation schemes

Abstract: This paper proposes building knowledge-based systems using a programming system based on a very-high-level language. It gives an overview of such a programming system, BC, and shows how BC can be used to implement knowledge representation features, providing as examples, automatic maintenance of inverse links and property inheritance. The specification language of BC can be extended to include a knowledge representation language by describing its knowledge representation features. This permits a knowledge-based program and its knowledge base to be written in the same very-high-level language which allows the knowledge to be more efficiently incorporated into the program as well as making the system as a whole easier to understand and extend.

Mental Representation of Circuit Diagrams.

Abstract: : This work is concerned with the knowledge that electronics technicians possess of electronic equipment, and more generally, with how people operate in tasks that draw upon a complex spatial symbolic knowledge base. A technician's knowledge base is postulated to consist of three types of related knowledge: (a) structural/functional knowledge, which pertains to the actual configuration of a circuit and the role that its components play in the operation of the device; (b) prototypical knowledge, which pertains to the general properties common to circuits of a given type; and (c) procedural knowledge, which pertains to the way that a circuit can be modified and to the interaction among knowledge elements of all three types of knowledge. Early phases of this work focused on a study of individual differences in structural knowledge and an experiment conducted to investigate individual differences in procedural knowledge. Novice and expert subjects performed tasks in which they had to either locate and correct an error in a circuit, change the function of a circuit, or complete a missing segment in a circuit. Keywords include: circuit diagrams; cognitive models; electronic circuits; expert systems; functional knowledge; individual differences; knowledge representation; mental representation; structural knowledge; symbolic knowledge; procedural knowledge; and procedural guidelines.