Dynamically Adjusting Concepts to Accommodate Changing Contexts

In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently , a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning , and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to eeciently accommodate changing contexts , a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modiication of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for comparison .