Using case-based reasoning for decision support

Case-based reasoning is one of the most preferred method for problem solving and decision making in complex and dynamically changing situations. Case-based reasoning solves problems by relating some previously solved problems or experiences to a current, unsolved problem in a way that facilitates the search for an acceptable solution. In this paper we propose a computational case-based reasoning model, and investigate its feasibility to decision support. A performance comparison among two computational models, including ours, and a symbolic model is also conducted. We view our model not as a replacement technology, but rather as a complementary technology to the symbolic approaches for the purpose of supporting unstructured-oriented decisions.<>