A learning interface agent for scheduling meetings
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
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Existing methods for constructive induction usually isolate feature generation from problem solving, and do not exploit information about the purpose for which features are created. This paper describes a theory of feature generation that creates features using both domain theory and feedback from a concept learner. An evaluation function can then be learned using these features that is able to direct a problem-solver. The theory has been implemented in a system called Zenith, which has been applied to two domains. Zenith is able to generate useful features for each domain, given only a domain theory and the ability to solve problems in the domain.