This book is the result of the author's research. The concepts it describes are illustrated in an expert recipe designer called CHEF. The book is well written, with many illustrations taken from CHEF dialogues and LISP code. It is not a textbook but would make a good reference for a college senior or first-year graduate class project. The author is somewhat repetitious, which is good if the reader is not familiar with the ideas of case-based reasoning.
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.