Approximations in Bayesian Belief Universe for Knowledge Based Systems
This addresses computational efficiency issues for expert systems in knowledge-based domains, though it is incremental as it builds on existing CPN methods.
The paper tackles the combinatorial explosion problem in large causal probabilistic networks (CPNs) by proposing an approximation scheme that excludes rarely occurring cases, leading to orders of magnitude improvement in resource consumption, with established error bounds and empirical validation on a real-world CPN.
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.