Approximations in Bayesian Belief Universe for Knowledge Based Systems

TLDR: In this paper, the authors proposed an approximation scheme that identifies rarely occurring cases and excludes these from being processed as ordinary cases in a CPN-based expert system. But this scheme is not suitable for real-world expert systems.

Abstract: When expert systems based on causal probabilistic networks (CPNs) reach a certain size and complexity, the "combinatorial explosion monster" tends to be present We propose an approximation scheme that identifies rarely occurring cases and excludes these from being processed as ordinary cases in a CPN-based expert system Depending on the topology and the probability distributions of the CPN, the numbers (representing probabilities of state combinations) in the underlying numerical representation can become very small Annihilating these numbers and utilizing the resulting sparseness through data structuring techniques often results in several orders of magnitude of improvement in the consumption of computer resources Bounds on the errors introduced into a CPN-based expert system through approximations are established Finally, reports on empirical studies of applying the approximation scheme to a real-world CPN are given