Evolutional concept learning from observations through adaptive feature selection and GA-based feature discovery

Abstract

This paper presents a method for concept formation of a personal learning apprentice (PLA) system that attempts to capture users' internal conceptual structure by observing interactions between user and system. The primary goal of a PLA system is to identify the users' cognition that underlies the taking of action. This is based on the capability to reconstruct internal concepts as behavior-shaping constraints by observing operations as well as the information presented by the system. 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 can 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.