Showing papers on "Knowledge acquisition published in 1985"

The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra

Abstract: The knowledge required to solve algebra manipulation problems and procedures designed to hasten knowledge acquisition were studied in a series of five experiments. It was hypothesized that, as occurs in other domains, algebra problem-solving skill requires a large number of schemas and that schema acquisition is retarded by conventional problem-solving search techniques. Experiment 1, using Year 9, Year 11, and university mathematics students, found that the more experienced students had a better cognitive representation of algebraic equations than less experienced students as measured by their ability to (a) recall equations, and (b) distinguish between perceptually similar equations on the basis of solution mode. Experiments 2 through 5 studied the use of worked examples as a means of facilitating the acquisition of knowledge needed for effective problem solving. It was found that not only did worked examples, as expected, require considerably less time to process than conventional problems, but that su...

The cognitive psychology of school learning

Abstract: 1. The Human Information Processing System. 2. Knowledge Representation. 3. Knowledge Acquisition. 4. Use of Knowledge. 5. School Subjects.

Strategies for Knowledge Acquisition

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.

A knowledge acquisition program for expert systems based on personal construct psychology

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.

MORE: an intelligent knowledge acquisition tool

Abstract: MORE is a tool that assists in eliciting knowledge from domain experts. Acquired information is added to a domain model of qualitative causal relations that may hold among hypotheses, symptoms, and background conditions. After generating diagnostic rules from the domain model, MORE prompts for additional information that would allow a stronger set of diagnostic rules to be generated, MORE'S primary value lies in its understanding of what kinds of knowledge are likely to be diagnostically significant. By formulating its questions in a way that focuses on such knowledge, it makes the most effective use of the domain experts' time.

R1-Soar: An Experiment in Knowledge-Intensive Programming in a Problem-Solving Architecture

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.

Knowledge Acquisition from Multiple Experts

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.

ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system

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.

Knowledge acquisition for constructive systems

Abstract: Over the past ten years, significant progress has been made in understanding how the knowledge acquisition process for classification systems can be automated. But during this period little attention has been paid to the problem of how to automate the knowledge acquisition process for systems that solve problems by constructing solutions. This paper describes SALT, a tool designed to assist with knowledge acquisition for configuration tasks. SALT assumes a problem-solving strategy involving stages of generate, test, backup, modify, and regenerate. It exploits this problem-solving strategy to guide its interrogation of domain experts and to represent the knowledge they provide in a way that insures it will be brought to bear whenever relevant.

Man-machine interface issues in the construction and use of an expert system

Abstract: The effectiveness and acceptability of an expert system is critically dependent on its man-machine interface. This paper uses a framework of three central man-machine interface issues: knowledge acquisition, knowledge representation and the communications interface, as a basis for evaluating a Prospector-type expert system shell. The application domain used as an example is a small system for fault finding on 11 GHz radio equipment. Long-term implications for the design of good man-machine interfaces for future expert systems are discussed and, where possible, shorter-term guidelines for knowledge engineers are offered.

SEEK2: a generalized approach to automatic knowledge base refinement

Abstract: This paper describes an approach to knowledge base refinement, an important aspect of knowledge acquisition. Knowledge base refinement is characterized by the addition, deletion, and alteration of rule-components in an existing knowledge base, in an attempt to improve an expert system's performance. SEEK2 extends the capabilities of its predecessor rule refinement system. SEEK [1], In this paper we describe the progress we have made since developing the original SEEK program: (a) SEEK2 works with a more general class of knowledge bases than SEEK, (b) SEEK2 has an "automatic pilot" capability, i.e., it can, if desired, perform all of the basic tasks involved in knowledge base refinement without human interaction, (c) a metalanguage for knowledge base refinement has been specified which describes both domain-independent and domain-specific metaknowledge about the refinement process.

EXPERT SYSTEMS: New Approaches to Computer-aided Engineering*

Abstract: This paper provides an overview of the burgeoning new field of expert (knowledge-based) systems This survey, is tutorial in nature, intended to convey thegestalt of such systems to engineers who are newly exposed to the field The discussion includes definitions, basic concepts, expert system architecture, descriptions of some of the programming tools and environments with which knowledge-based systems can be built, and approaches to knowledge acquisition Some currently extant expert systems are describeden passant, including a few developed for engineering purposes Comments follow on the engineering of knowledge, as both cultural and social processes The paper closes with an assessment of the roles that expert systems can play in engineering analysis, design, planning, and education

Knowledge Acquisition for Expert Systems: Experience in Leukaemia Diagnosis

Abstract: EMYCIN was used to develop an expert system for the interpretation of immunological data obtained in the cell surface phenotyping of leukaemia. Access to a recognised expert, and a large quantity of data on the leukaemias, has facilitated a systematic study of knowledge acquisition and knowledge base refinement based on tape recorded commentaries made by the expert. System performance was analysed at six stages in its development, and ways in which it differed from that of the human diagnostician were identified. Among the most suggestive observations were differences in the way that “undiagnosable” patients were treated and a failure of the elicitation technique to reveal structural aspects of the task. The tools and techniques of knowledge engineering are a significant advance, but a better methodology for developing high quality knowledge bases is needed.

Gradualness Facilitates Knowledge Refinement

Abstract: To facilitate knowledge refinement, a system should be designed so that small changes in the knowledge correspond to small changes in the function or performance of the system. Two sets of experiments show the value of small, heuristically guided changes in a weighted rule base. In the first set, the ordering among numbers (reflecting certainties) makes their manipulation more straightforward than the manipulation of relationships. A simple credit assignment and weight adjustment strategy for improving numbers in a weighted, rule-based expert system is presented. In the second set, the rearrangement of predicates benefits from additional knowledge about the ``ordering'' among predicates. A third set of experiments indicates the importance of the proper level of granularity when augmenting a knowledge base. Augmentation of one knowledge base by analogical reasoning from another knowledge base did not work with only binary relationships, but did succeed with ternary relationships. To obtain a small improvement in the knowledge base, a substantial amount of structure had to be treated as a unit.

Representing procedural knowledge in expert systems: an application to process control

Abstract: The paper presents a novel expert system architecture which supports explicit representation and effective use of both declarative and procedural knowledge These two types of expert knowledge are represented by means of production rules and event-graphs respectively, and they are processed by a unified inference engine Communication between the rule level and the event-graph level is based on a full visibility of each level on the internal state of the other, and it is structured in such a way as to allow each level to expert control on the other This structure offers several advantages over more traditional architectures Knowledge representation is more natural and transparent; knowledge acquisition turns out to be easier as pieces of knowledge can be immediately represented without the need of complex transformation and restructuring; inference is more effective due to reduced non-determinism resulting from explicit representation of fragments of procedural knowledge in event-graphs; finally, explanations are more natural and understandable The proposed architecture has been adopted for the design of PROP, an expert system for on-line monitoring of the cycle water pollution in a thermal power plant PROP is running on a SUN-2 workstation and has been tested on a sample of real cases

A case study in structured knowledge acquisition

Abstract: Building an expert system usually comprises an entangled mixture of knowledge acquisition and Implementation efforts. An emerging methodology based on cognitive psychology and software development guides and supports knowledge acquisition while Implementation is deferred. This allows for a more deliberate choice of architecture. This paper presents a case stud/to test the methodology.

Towards a model of conceptual knowledge acquisition through directed experimentation

Abstract: Most current Artificial Intelligence systems require a complete and correct model of their domain of application. However, for any domain of reasonable size, it is not feasible to construct such a model. The main thrust of this project is to build a system that can continuously update its model through a constant monitoring of the real world. The project involves the development of a system that starts with an incomplete and incorrect model of the world. While performing its tasks the system is occasionally confronted by observations which are inconsistent with its current beliefs. It attempts to explain these observations by hypothesizing reasons for the inconsistencies and devising experiments to pinpoint the flawed belief. Based on the results of the experiments the system revises its beliefs to accommodate the previously inconsistent observations.

Inductive knowledge acquisition for rule-based expert systems

Abstract: The RL program was developed to construct knowledge bases automatically in rule-based expert systems, primarily in MYCIN-like evidence-gathering systems where there is uncertainty about data as well as the strength of inference, and where rules are chained together or combined to infer complex hypotheses. This program comprises three subprograms: (1) a program that learns confirming rules, which employs a heuristic search commencing with the most general hypothesis; (2) a subprogram that learns rules containing intermediate concepts, which exploits the old partial knowledge or defines new intermediate concepts, based on heuristics; (3) a program that learns disconfirming rules, which is based on the expert''s heuristics to formulate disconfirming rules. RL''s validity has been demonstrated with a performance program that diagnoses the causes of jaundice.

Knowledge based expert systems for medical diagnosis.

Abstract: Knowledge based expert systems' have been developed in the last decade for many different applications by adopting artificial intelligence techniques. The paper discusses the main characteristics of the expert systems devoted to medical diagnosis (knowledge representation, explanation capability, inexact reasoning) and addresses some of the limitations (mainly system validation and knowledge acquisition). Finally the paper sketches the overall organization of an expert system devoted to the evaluation of liver function.

Knowledge engineering revisited

Abstract: About six years ago a paper on Knowledge Engineering was published in this Journal. This paper attempts to extend and elaborate on the ideas and concepts discussed earlier. Four major problems are addressed: Preservation of knowledge, proliferation of knowledge, dissemination of knowledge, and application of knowledge. The design principles for both rule-based expert systems and pattern-directed expert systems are discussed and compared.

Knowledge acquisition by computer induction

Abstract: The generation of Expert Systems requires that the specialized knowledge of an expert be transferred to a computer system in a form suitable for machine interpretation. In many knowledge domains it is difficult for experts to describe their knowledge in an appropriate form. Where such expert knowledge is difficult to describe it is often possible to obtain good examples of the expert's decisions. Newly emerging computing techniques allow the induction of decision rules from examples; rules which can often effectively model the decision making behaviour of the expert in such situations. The use of such techniques provides a powerful new tool for the construction of Expert Systems.

A logic-based knowledge source system for natural language documents

Abstract: A methodology is described for creating and structuring a knowledge base system for storing and intelligently retrieving the types of knowledge found in regulations, specifications and other similar documents. The approach involves translating the document into a simple, formal, English-like rule-based language. The language combines production rules with constraints, hierarchies and scripts. Our methodology structures and guides the process of translation and verification using several software tools. The retrieval process is based on associative indexing of rules and facts. Though our current domain is government travel regulations, the method is general enough to be useful for most technical material, such as software descriptions, design or service specifications, or parts of training manuals.

Research as intellectual property: influences within the university.

Abstract: If language changes to accommodate new experience, it is not difficult to deduce the type of experience which underlies the term "intellectual property." Fifteen years ago, there were few parts of the academic world where that term, if indeed it was known at all, would have been viewed as anything other than alien and unwelcome. Today, the idea that the products of the mind constitute a kind of property-and valuable property at thatis part of common campus discourse, and questions about that particular kind of property have become vexing and controversial issues of academic policy. For U.S. universities, the emergence of intellectual property as an important source of real economic value raises questions of two kinds. The first, and most important, is how to prevent the effort to capture that value from undermining the central commitment of universities to the freest sharing of the fruits of knowledge acquisition. The second is how best to share the value that is created by research among those who are entitled to a portion of it: namely, the faculty, who are the creators of knowledge; universities, which are hosts to its creation; and government and industry, which are its principal sponsors. This article addresses those two issues-or, more accurately, clusters of issues. The first of them, the protection of open scientific communication, is by no means new to universities, although contemporary experience has produced some novel manifestations. The second, how to distribute the economic value created by knowledge, is new, at least in its scope and in the size of the economic stakes at issue. American universities are among the most permeable of social institutions. We have made them into instruments to fulfill purposes as diverse as training for the agricultural and industrial revolutions, curing cancer, giving life to a national commitment to equal opportunity without regard to race or ethnic origin, and, most recently, helping to repair the competitive disadvantage that America suffers in comparison to some of its main trading partners. On the whole, universities have flourished as they have responded to such high expectations. The willingness to respond has both produced intellectual stimulation and called forth resources that might otherwise not have been available. At times, too, the response to society's wishes has led to an unhealthy stretching of the fabric of the university, to assuming responsibilities that are not properly academic, and to adopting practices that are inconsistent with academic norms. The recent rebirth of mutual interest between universities and industry calls this history to mind. For reasons that will be discussed shortly, we may expect universities to respond to-indeed, even to encourage-that interest, and we may expect industry to respond. We may also expect problems in the relationship, and if history is a guide, some of those problems will test the ability of universities to respond to social need without relinquishing those values that define them as institutions. Chief among those values is that of openness. Although the value is an abstraction, the practices Robert M. Rosenzweig is President of the Association of American Universities, 1 Dupont Circle, N. W., Washington, DC 20036. * This essay was produced with the support of funds from the National Science Foundation and the National Endowment for the Humanities under NSF Grant RII-8309874.

A prototype manufacturing knowledge base in Syllog

Abstract: This paper describes a prototype knowledge base for manufacturing planning, which we have built using a knowledge system shell called Syllog. We describe a Tester Capacity Planning and Yield Analysis task, knowledge needed for a part of the task, and the use of the knowledge in the Syllog system. We report that the sometimes difficult process of knowledge acquisition turned out, in this case, to be straightforward. Knowledge acquisition and knowledge use are done in the same language in Syllog.

Knowledge Acquisition for ATE Diagnosis.

Abstract: An applied intelligence program for ATE fault diagnosis shows promise as an effective method to reduce mean time to repair (MTTR). The types of knowledge required by an intelligent diagnostic for VLSI test systems, the resources needed to derive that knowledge, the approach implemented to organize it, and the final form of the knowledge representations which resulted from our work are discussed in this paper.

Preceptor: a shell for medical expert systems and its application in a study of prognostic indices in stroke

Abstract: Preceptor is a shell system for building rule-based expert systems to support clinical decision-making. Its highly-structured knowledge base is designed to reflect the variety of decision-making activities in patient management. We describe Preceptor's knowledge base and dialogue structures, its inference strategies, and its comprehensive facilities for knowledge base creation and management. We report its application in a study of prognostic indices in stroke, and describe our approach to knowledge acquisition in this application.