Proceedings of the Annual Meeting of the Cognitive Science Society

A computational model of learning in a complex domain is described an its implementation is discussed. The model supports knowledge-based acquisition of problem-solving concepts from observed examples, in the domain of physics problem-solving. The system currently learns aobut momentum conservation, in a psychologically plausible fashion form a background knowledge of Newton's laws and the calculus. In its contribution to machine learning, this research is important for artifical intelligence. From a psychological perspective it demonstrates the computational consistency of a machanism tha tmay underlie human learning iin a complex domain. This work also has implications for computer-adied instruction, in that it advances a learning model for a complicated domain involving both symbolic and numerical reasoning.