Proceedings of the Annual Meeting of the Cognitive Science Society

Eureka is a problem-solving system that operates through a form of analogical reasoning. The system was designed to study how relatively low-level memory, reasoning, and learning mechanisms can account for high-level learning in human problem solvers. Thus, Eureka's design has focused on issues of memory representation and retrieval of analogies, at the expense of complex problem-solving ability or sophisticated analogical elaboration techniques. Two computational systems for analogical reasoning, ARCS/ACM E and MAC/FAC, are relatively powerful and well-known in the cognitive science literature. However, they have not addressed issues of learning, and they have not been implemented in the context of a performance task that can dictate what makes an analogy "good". Thus, it appears that these different research directions have much to offer each other W e describe the Eureka system and compare its analogical retrieval mechanism with those in ARC S and MAC/FAC. W e then discuss the issues involved in incorporating ARC S and MAC/FAC into a learning problem solver such as Eureka.