Learning and Representation Change

Jeffrey C. Schlimmer

To remain effective without human interaction, intelligent systems must be able to adapt to their environment. One useful form of adaptation is to incrementally form concepts from examples for the purposes of inference and problem-solving. A number of systems have been constructed for this task, yet their capability is limited by the language used to represent concepts. This paper presents an extension to the concept acquisition system STAGGER that allows it to utilize continuously valued attributes. The combination of methods employed is able to dynamically acquire appropriate representations, thereby minimizing the impact of initial representational bias decisions. Of additional interest is the distinction between the computational flavor of the learning methods, for one is similar to connectionist approaches while the other two are of a more symbolic nature.

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