Incremental Learning for Improved Decision Support in Knowledge Based Design Systems

In this paper, LEAR, a concept learning system for decision support in routine design tasks, is presented. LEAR learns incrementally from the instances which are generated during normal design sessions with a host CAD system. To achieve this an existing learning method was adapted to task specific constraints and complemented with several novel approaches to knowledge organization. This includes a flexible distributed concept representation which exploits background knowledge to reduce the complexity of the learning process as well as an efficient method for constructive induction which uses fuzzy sets to determine the most suitable high level attributes.

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