Case-Based Reasoning in Mixed Paradigm Settings and with Learning
Edwina L. Rissland · 1994
In this project we investigated: (1) CBR in mixed paradigm settings, in particular in a blackboard-based system, called FRANK, that generated various types of explanations and arguments where supporting tasks, such as case-and rule-based reasoning, were dynamically configured to reflect the user's intended purposes for the report: (2) pure CBR, particularly issues concerning the use of multiple indices and types of case representations, in a system called BankXX. that used classic heuristic best-first search to retrieve information needed for case-based argument: and (3) the application of machine learning techniques to core issues in CBR, such as the problems of learning indices and prototype cased and estimating concept theory drift.