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The problems associated with scaling involve active and challenging researchtopics in the area of artificial intelligence. The purpose is to solve realworld problems by means of AI technologies, in cases where the complexity ofrepresentation of the real world problem is potentially combinatorial. In thispaper, we present a novel approach to cope with the scaling issues in Bayesianbelief networks for ship classification. The proposed approach divides theconceptual model of a complex ship classification problem into a set of smallmodules that work together to solve the classification problem while preservingthe functionality of the original model. The possible ways of explaining sensorreturns (e.g., the evidence) for some features, such as portholes along thelength of a ship, are sometimes combinatorial. Thus, using an exhaustiveapproach, which entails the enumeration of all possible explanations, isimpractical for larger problems. We present a network structure (referred to asSequential Decomposition, SD) in which each observation is associated with aset of legitimate outcomes which are consistent with the explanation of eachobserved piece of evidence. The results show that the SD approach allows one torepresent feature-observation relations in a manageable way and achieve thesame explanatory power as an exhaustive approach.

A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification