  The problems associated with scaling involve active and challenging research
topics in the area of artificial intelligence. The purpose is to solve real
world problems by means of AI technologies, in cases where the complexity of
representation of the real world problem is potentially combinatorial. In this
paper, we present a novel approach to cope with the scaling issues in Bayesian
belief networks for ship classification. The proposed approach divides the
conceptual model of a complex ship classification problem into a set of small
modules that work together to solve the classification problem while preserving
the functionality of the original model. The possible ways of explaining sensor
returns (e.g., the evidence) for some features, such as portholes along the
length of a ship, are sometimes combinatorial. Thus, using an exhaustive
approach, which entails the enumeration of all possible explanations, is
impractical for larger problems. We present a network structure (referred to as
Sequential Decomposition, SD) in which each observation is associated with a
set of legitimate outcomes which are consistent with the explanation of each
observed piece of evidence. The results show that the SD approach allows one to
represent feature-observation relations in a manageable way and achieve the
same explanatory power as an exhaustive approach.
