The research component involves case-based reasoning (CBR) a technique that stores and later applies cases of known problems and their solutions to solve new problems. Since few stored cases will exactly match a new problem, most cases will be related at best by analogy to the new one. In that case, it is necessary first to retrieve an appropriate analogous case, check to see that it matches the new problem in some essential ways, then adapt it to the new problem in such a way as to infer a solution. In storing and using cases, it is necessary to do some abstraction, either at the time that they are stored away for later use or at the time retrieved, to avoid having to store large numbers of very similar but slightly different cases. This project examines the advantages of developing a hierarchy of abstraction for each stored case, a technique called "stratified CBR". The stratified CBR technique will be applied to a typical search task (route planning), two configuration tasks (artifact design and goal-directed activity planning), and an analytical task (legal reasoning), in order to test hypotheses about the superiority of the stratified CBR technique and to gain experience in using the technique. During the period of the grant, an educational plan will be developed that exposes students to the insights, perspectives, and tools of AI by (1) educating undergraduates about AI's basic methods and concepts and about AI's philosophical, social, and cognitive consequences, and (2) educating graduate students in other fields about the AI techniques and tools applicable to their fields.
