Dynamically Adjusting Categories to Accommodate Changing Contexts

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 12

Student Abstracts

Concept formation is the process by which generalizations are formed through observation of instances from the environment. These instances are described along a number of attributes, which are selected according to their relevance to the problem or task for which the concepts will be used. The context of a concept learning problem consists of the goals and tasks of the learner, as well as its background knowledge and domain theories and the external environment in which it operates. Context is essential to inductive concept learning for it determines which attributes to use for a given problem out of the infinitely many available, providing a bias for the learner (Mitchell, 1980). Furthermore, context is not a static entity, but is constantly changing, especially in the types of learning tasks faced by humans (e.g. Seifert 1989, Barsalou 1991). As concept formation systems are employed in tasks more typical of natural domains and "real-world" problems, the ability to respond to changing contexts becomes increasingly important.