Risk-driven intent assessment and response generation in maritime surveillance operations

摘要

Decision support systems (DSSs) are playing an increasingly important role in the characterization of suspicious activities in an area of interest given their proved ability to turn vast amounts of raw data into actionable intelligence that is easy to understand by human operators. Although risk management is an integral component of the decision making process that directly contributes towards improved situational awareness and response assessment, an active end-to-end consideration of the underlying risk sources in the environment is still an important feature that most DSSs currently lack. Additionally, deciding on an appropriate course of action (COA) to mitigate emerging threats in the system is a challenging task even for domain experts given that (1) the number of potential responses to analyze could be overwhelmingly large; (2) seldom are those responses judged in terms of the risks associated with their enactment and (3) assessing the effectiveness of the potential responses in the real world is usually time-consuming and simulation-driven. In this paper, we formalize the adaptation of a recently proposed Risk Management Framework to account for behavioral intents associated with the objects of interest (OOIs) in the monitoring environment and their link to automatic response generation. The intent of the objects is inferred from high-level cognitive and behavioral knowledge in the form of anomalies. When an OOI has crossed a permissible risk threshold, we demonstrate how responses to that situation can be automatically elicited by the COA recommendation module of a risk-aware DSS. Multicriteria decision analysis (MCDA) is used to judge a diverse set of plausible responses according to different operational objectives. We illustrate the application of the proposed framework in the context of maritime surveillance operations by triggering a corporate search for a missing vessel. To the best of our knowledge, this is the first time that risk features are syn- hesized from anomalies and integrated into a more comprehensive RMF engine for knowledge (response) elicitation.