Bayesian case-based reasoning with neural networks

Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. A neural network architecture is introduced for efficient CBR. It is shown how a rigorous Bayesian probability propagation algorithm can be implemented as a feedforward neural network and adapted for CBR. In the authors´ approach, the efficient indexing problem of CBR is naturally implemented by the parallel architecture, and heuristic matching is replaced by a probability metric. This allows their CBR to perform theoretically sound Bayesian reasoning. It is shown how the probability propagation actually offers a solution to the adaptation problem in a very natural way