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We are designing and implementing an integrated programming framework to assist application program developers with the automation of a broad range of tasks. Our framework encourages the following activities:• analyzing the situation in which automation is going to be introduced, • capturing the results of the analysis as a model, • building a workflow application program to manage...
We present an induction algorithm, KATE, whose learning strategy is similar to the ID3 algorithm but which can handle examples described by several object, relations between objects, and use background domain knowledge to constrain the search space. The efficient numeric learning techniques used in ID3 have been combined with a rich symbolic knowledge representation language (frames) which allows...
This paper presents a number of core issues that are seen as fundamental to the success and well-being of the knowledge engineering enterprise. In particular, it examines the problems that dominate the current state of the art in knowledge acquisition (KA). These include: the development of KA methodologies, the construction of software support tools for KA, the integration of knowledge acquired from...
The main goal of this paper is to explore the possibilities of exploiting psychological methods for the purpose of knowledge engineering. Hypotheses are presented why both the pure “psychological” and the pure “engineering” positions are not viable for building expert systems. A “middle-out” strategy is proposed that preserves the best of both worlds while minimizing the problems of each. This “middle-out”...
The problems arising during the early steps of knowledge acquisition are similar to problems in social research based on the interpretative paradigm. Therefore the article shows the transfer of principles, methods, and techniques from social science to knowledge acquisition. This transfer offers a methodological foundation for the gathering and interpretation of knowledge and a framework which guides...
This chapter outlines two different yet complementary approaches to enhance cognitive adequacy in the process of knowledge engineering: model-based knowledge acquisition and case-based reasoning. Although both differ with respect to methods, goals and scientific background, arguments are advanced that a linkage of both approaches will result in a significant contribution to the methodology of knowledge...
During the course of the development of a Case-Oriented Expert System for situated applications additional cases were needed. The required cases were obtained by having a human expert refit old solutions to new problems and the structural relations between source and target cases were analyzed: A higher degree of reuse of the old cases was found when the expert could apply derivational reasoning and...
Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation-based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an...
This paper presents a hypermedia, domain independent system supporting the acquisition, formalization, and representation of cases. The system assists a knowledge engineer in a step by step transformation of an informally represented case into a formally represented one. Each case consists of five components: context, task, solution trace, solution and evaluation of the solution. The knowledge engineer...
Our work is concerned with a central process in knowledge acquisition for case-based expert systems: understanding examples of expert's problem solving traces, in our case worked-out examples in physics textbooks. Based on evidence from psychological research, an active, expectation-driven strategy for example processing is developed. The strategy deals with the initial phases of learning, exploiting...
As a supplementation to other papers within this chapter on case-based approaches to Knowledge Engineering, we discuss some general aspects of case-based reasoning. We differentiate it from other case-using approaches and argue for the use of case-based reasoners within integrated knowledge engineering environments.
It is argued that knowledge engineering should take a cognitive stance, i.e. it should aim for cognitively adequate systems. The notion of cognitive adequacy is unfolded from an idealized, absolutely strong meaning (i.e., a complete model of a human expert) down to the very weak notion of conforming to recognized ergonomic standards. Various ways are proposed to enhance cognitive adequacy in the model-based...
When knowledge engineering is consequently looked upon as scientific discovery, certain prescriptions can be derived about how to observe expertise and how to deal with intermediate stages of the modelling process. These prescriptions concern roles played by underlying assumptions, theories and paradigms of cognitive science and their respective implications upon conduction and interpretation of individual...
After a short overview of knowledge acquisition highlights, we review experiences that we had in our knowledge acquisition project. We conclude that automated knowledge acquisition does not work without a documentation of the purpose that the knowledge will fulfill once it is acquired. This can be done for example through a description of a method of problem-solving. The remainder of the paper gives...
(1) Are the well-known “strong” problem solving methods in expert systems cognitively adequate enough for the experts which have to formalize their knowledge accordingly? and (2) How significant are adequate graphical representations offered by some knowledge acquisition tools for the internal model of the experts?
The goal of knowledge engineering is to create an artificial system which reflects knowledge-like qualities. Current tools, techniques and procedures in knowledge engineering concentrate on the elicitation and representation of knowledge structures. This concentration of effort reflects the current emphasis on the epistemological and computational/representational characteristics of knowledge engineering...
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