This project is being funded through the Learning and Intelligent Systems (LIS) Initiative. Concepts are essential for intelligent thought and action. The goal of the project is an integrated view of concept learning in humans and machines. The primary focus will be combining psychological experimentation with artificial intelligence modeling to examine the interaction of world knowledge and empirical information during concept learning. The representation of concepts consists of feature regularities observed in the instances and of features inferred from world knowledge. However, current theories focus on only one type of feature and do not consider how learning each might affect the other. Additional work will examine how the use of concepts (such as those used for problem solving) may affect learning, how prior knowledge may be restructured to accommodate new information, and how concepts may change with age and experience. Computational learning theory will be adapted to provide a mathematical characterization of the learning process. The view of concept learning that results from this work will be integrated in that it will (a) investigate and account for a wide variety of concept learning results that are often studied separately, and (b) pool the research strengths of psychology, artificial intelligence machine learning, and computational learning theory. The first goal will place greater constraints on theoretical accounts, suggest new possibilities, and help to decide among competing explanations. The second goal will lead to a theory that is psychologically and computationally plausible, yet sufficiently rigorous to be analyzed with the mathematical tools of computational learning theory. Such a theory will contribute to the generation of new knowledge by broadening the understanding of concept learning in each of the fields, and by promoting new research issues and approaches in each field through interdisciplinary work.
