1 Introduction
Global maritime surveillance involves the monitoring of several hundred vessels in many cases. Using existing sensors, it is possible to monitor an individual ship very effectively if we are aware that it may pose a threat to security. However, due to the volume of information with which we are faced, it is often difficult for a human observer to determine which ships should be subjected to detailed monitoring. As such, the study of automated anomaly detection systems has emerged as an important topic in maritime surveillance. In this paper, we present an approach to anomaly detection based on a formal model of belief change that has been developed in the Artificial Intelligence community. We present our approach as a high-level architecture, built to complement an existing rule-based expert system for anomaly detection [13].