Knowledge acquisition for expert systems
Knowledge, skill and artificial intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Artificial intelligence and scientific method
Artificial intelligence and scientific method
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Expert Systems
Case-Based Reasoning in Design
IEEE Expert: Intelligent Systems and Their Applications
The "What" and "How" of Learning in Design
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Special Issue: Machine Learning in Design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Soft computing in engineering design - A review
Advanced Engineering Informatics
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A crucial early stage in the engineering design process is the conceptual design phase, during which an initial solution design is generated. The quality of this initial design has a great bearing on the quality and success of the produced artefact. Typically, the knowledge required to perform this task is only acquired through many years of experience, and so is often at a premium. This has led to a number of attempts to automate this phase using intelligent computer systems. However, the knowledge of how to generate designs has proved difficult to acquire directly from human experts, and as a result, is often unsatisfactory in these systems. The application of inductive machine learning techniques to the acquisition of this sort of knowledge has been advocated as one approach to overcoming the difficulties surrounding its capture. Rather than acquiring the knowledge from human experts, the knowledge would be inferred automatically from a set of examples of the design process. This paper describes the authors' investigations into the general viability of this approach in the context of one particular conceptual design task, that of the design of fluid power circuits. The analysis of a series of experiments highlights a number of issues that would seem to arise regardless of the working domain or particular machine learning algorithm used. These issues, presented and discussed here, cast serious doubts upon the practicality of such an approach to knowledge acquisition, given the current state of the art.