A constrained architecture for learning and problem solving

This paper describes EUREKA, a problem-solving architecture that operates under strong constraints on its memory and processes. Most significantly, EUREKA does not assume free access to its entire long-term memory. That is, failures in problem solving may arise not only from missing knowledge, but from the (possibly temporary) inability to retrieve appropriate existing knowledge from memory. Additionally, the architecture does not include systematic backtracking to recover from fruitless search paths. These constraints significantly impact EUREKA's design. Humans are also subject to such constraints, but are able to overcome them to solve problems effectively. In EUREKA's design, we have attempted to minimize the number of additional architectural commitments, while staying faithful to the memory constraints. Even under such minimal commitments, EUREKA provides a qualitative account of the primary types of learning reported in the literature on human problem solving. Further commitments to the architecture would refine the details in the model, but the approach we have taken de-emphasizes highly detailed modeling to get at general root causes of the observed regularities. Making minimal additional commitments to EUREKA's design strengthens the case that many regularities in human learning and problem solving are entailments of the need to handle imperfect memory.