Memory-Based Learning

Derivational analogy: A theory of reconstructive problem solving and expertise acquisition


CMU-CS-85-115, Carnegie Mellon University. Reprinted in Michalski, R. S., Carbonell, J. G., and Mitchell, T. M., (Eds.), Machine Learning: An Artificial Intelligence Approach, volume 2, chapter 14, pages 371-392. Morgan Kaufmann Publishers. Derivational analogy, a method of solving problems based on the transfer of past experience to new probiem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise acquisition is discussed.

The simulation of verbal learning behavior


The purpose of this report is to describe in detail an informationProcessing model of elementary human symbolic learning processes. Thismodel is realized by a computer program called the Elementary Perceiverand Memorizer (EPAM).The EPAM program is the precise statement of an information processingtheory of verbal learning that provides an alternative to other verballearning theories which have been proposed.1 It is the result of an attemptto state quite precisely a parsimonious and plausible mechanism sufficientto account for the rote learning of nonsense syllables. The criticalevaluation of EPAM must ultimately depend not upon the interest whichit may have as a learning machine, but upon its ability to explain andPredict the phenomena of verbal learning. Proceedings of the Western Joint Computer Conference, 1961, 19:121-132. Reprinted in Feigenbaum & Feldman, Computers and Thought (1963).