Extending the indexing vocabulary of case based reasoning with task specific features

Abstract

One of the central issues in case-based reasoning is the choice of an indexical vocabulary that allows for e f i -cient retrieval of experiential knowledge from memory. Presently, most systems use a vocabulary of domain concepts specific to the application domain (medicine, engineering, etc...). This paper proposes a new additional indexical Vocabulary based on features of abstract problem types(planning, diagnosis, etc.. .). The rationale f o r such a vocabulary is supported by empirical data gathered in the domain of alloantibody identification, and by the interpretation of this data in terms of the computational complezity of abductive reasoning. Finally a case-based reasoning model of abduction is proposed that integrates both domain and problem type specific indexical vocabulary. The advantages of this model in terms of memory retrieval, case adaptation and problem solving are discussed.Area :Diagnosis, medicine. AI Topics: Knowledge representation, case based reasoning, problem solving. Impact: The Generic Task(GT) paradigm has been a successful approach to knowledge based systems engineering. The work presented in this paper is an extension of the G T paradigm to case based reasoning.