Abstract
A personal learning apprentice system such as an interface agent has to be able to selectively pick up regularities contained in a stream of actual observations as well as be able to construct a user's concept by actively inferring what the user is supposed to perceive from his/her apparent behaviors. We propose a conceptual learning method for such an agent using an evolutinary computation. Our proposed algorithm comprises two processes; adaptive feature selection and GA-based feature discovery. The former selects the essential attributes out of a provided set of attributes that may initially be either relevant or irrelevant, and the latter constructs new attributes using genetic algorithms applied to a set of elementary features logically represented in a disjunctive normal form. Our method cab be applied to artificial data as well as to a data set obtained from human-machine interactions observed during operation of a simulator of a generic dynamic production process.
