Luger G. F., Lewis J. & Stern C. (2002) Problem solving as model refinement: Towards a constructivist epistemology. Brain, Behavior and Evolution 59(1–2): 87–100. https://cepa.info/7499
Problem solving as model refinement: Towards a constructivist epistemology.
Brain, Behavior and Evolution 59(1–2): 87–100.
Fulltext at https://cepa.info/7499
In recent years the Artificial Intelligence research group at the University of New Mexico have considered several areas of problem solving in interesting and complex domains. These areas have ranged from the low level explorations of a robot tasked to explore, map, and use a new environment to the development of very sophisticated control algorithms for the optimal use of particle beam accelerators. Although the results of our research have been reflected in computer-based problem solvers, such as the robot discovering and mapping out its world, these computational tasks are in many ways similar to expert human performance in similar tasks. In fact, in many important ways our computer-based approach mimics human expert performance in such domains. This paper describes three of these task domains as well as the software algorithms that have achieved competent performances therein. We conclude this paper with some comments on how software models of a domain can elucidate aspects of intellectual performance within that context. Furthermore, we demonstrate how exploratory problem solving along with model refinement algorithms can support a constructivist epistemology.