Current Trends in Knowledge Acquisition

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Bob Wielinga, John Boose, B. Gaines, Maarten Van Someren
IOS Press, 1990 - Computers - 375 pages
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Knowledge acquisition has become a major area of artificial intelligence and cognitive science research. The papers in this book show that the area of knowledge acquisition for knowledge-based systems is still a diverse field in which a large number of research topics are being addressed. However, several main themes run through the papers. First, the issues of integrating knowledge from different sources and K.A. tools is a salient topic in many papers. A second major topic in the papers is that of knowledge modelling. Research in knowledge-based systems emphasises the use of generic models of reasoning and its underlying knowledge. An important trend in the area of knowledge modelling aims at the formalisation of knowledge models. Where the field of knowledge acquisition was without tools and techniques years ago, now there is a rapidly growing body of techniques and tools. Apart from the integrated workbenches already mentioned above, several papers in this book present new tools. Although knowledge acquisition and machine learning have been considered as separate subfields of AI, there is a tendency for the two fields to come together. This publication combines machine learning techniques with more conventional knowledge elicitation techniques. A framework is presented in which reasoning, problem solving and learning together form a knowledge intensive system that can acquire knowledge from its own experience.
 

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Contents

A Computational Model of KnowledgeIntensive Learning
1
Multiple Knowledge Acquisition Strategies in MOLTKE
21
Shelley Computer Aided Knowledge Engineering
41
Supporting Formal Specifications
60
Capturing Design Knowledge for Engineering Trade Studies
78
Producing Visuallybased Knowledge Specifications
105
Integration of Knowledge from Different
123
Development of Second Generation Knowledge
143
An Automated Laddering Tool
222
A Flexible SixStep Program for Defining
237
Methodological Foundations of Keats
257
Decision Tree Induction using Domain Knowledge
276
Towards Knowledge Acquisition from Domain Books
289
CaseOriented Knowledge Acquisition from Texts
302
Cases Models or Compiled Knowledge
339
First Order Logic Foundation of the KADS Conceptual Model
356

Validation of User Intention
161
Comparison of Inductive and Naive Bayesian Learning Approaches
190

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