COMBINING CASE-BASED REASONING AND STATISTICAL-METHOD FOR PROPOSING SOLUTION IN RICAD

Most case-based reasoning (CBR) systems concentrate on retrieving case s which are most similar to a case at hand. When a similar case is fou nd, the system will proceed to adapt (or modify) this solution to solv e the case at hand. This method of problem solving cannot be easily ap plied in our real-world problem domain (i.e. insurance). In this domai n, sufficient number of similar cases have to be retrieved so that the system could confidently calculate the final solution. More than one similar case must be retrieved due to the fact that most of the cases which are similar to the one at hand almost always contain inconsisten t results. This paper describes a CBR system called risk cost adviser (RICAD) which applies a statistical function in order to propose a rel iable answer. RICAD differs from other CBR systems as, in most cases, in addition to the use of the statistical function, it has to repeat i ts reasoning process until an adequate number of cases are collected t o calculate the answer. (C) 1997 Elsevier Science B.V.