A hybrid metareasoning architecture combining case-based reasoning and Bayesian networks

In complex domains, a single type of knowledge and reasoning method is often not sufficient for a decision support system to address the variety of tasks a user performs. It is often necessary to determine which reasoning method would be the most appropriate for each task, and a combination of different methods has often shown the best results. We examine the strengths and weaknesses of two complementary reasoning methods, case-based reasoning and Bayesian networks, and discuss how they can be combined to form a more robust and better-performing hybrid. We present a metareasoning system for automatically selecting the most viable reasoning method for a particular input query at runtime, for a sustained learning scenario where an expert provides initial domain knowledge through modeling illustrative cases.