Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between past experiences and new problems that impede reuse. First, this research describes the variety of mismatches that can arise and proposes a new approach to derivational analogy that uses appropriate adaptation strategies for each. Second, it compares this approach with seven others in a common domain. This empirical study shows that derivational analogy is almost always more efficient than problem solving from scratch, but the amount it contributes depends on its ability to overcome mismatches and to usefully interleave reuse with from-scratch problem solving. Finally, this research describes a fundamental tradeoff between efficiency and solution quality, and proposes a derivational analogy algorithm that can improve its adaptation strategy with experience

PS

Artificial Intelligence Journal, Vol. 67, 2 (1994), pp. 287--328.

@article{blumenthal:aij94, title={Analysis and Empirical Studies of Derivational Analogy}, author={B. Blumenthal and Bruce Porter}, volume={67}, journal={Artificial Intelligence Journal}, number={2}, pages={287--328}, url="http://www.cs.utexas.edu/users/ai-lab?blumenthal:aij94", year={1994} }