Situation Assessment (SA) is a key process in Information Fusion (IF) for security systems. The observed scenario generally involves multiple entities and actors, with possibly only a few being under the direct control of the decision-maker. SA aims at explaining the observed events (mainly) by establishing the entities and actors involved, understanding the relations existing between them, the surrounding environment, and past and present events. It is therefore evident how the SA process inherently hinges on understanding and reasoning about relations. This task is particularly demanding and important for dynamic large-scale scenarios such as those related to border and port security, where suspicious activities need to be detected as the needle in a haystack of largely predominant "pattern of life" activities involving many entities and actors. In this chapter, we highlight the capabilities of the recent Statistical Relational Learning framework of Markov Logic Networks for SA in maritime scenarios, and provide some examples and practical advice on their use, further detailing our previous work.

Reasoning with Relational Models for Maritime Domain Security

SNIDARO, Lauro;VISENTINI, Ingrid
2016

Abstract

Situation Assessment (SA) is a key process in Information Fusion (IF) for security systems. The observed scenario generally involves multiple entities and actors, with possibly only a few being under the direct control of the decision-maker. SA aims at explaining the observed events (mainly) by establishing the entities and actors involved, understanding the relations existing between them, the surrounding environment, and past and present events. It is therefore evident how the SA process inherently hinges on understanding and reasoning about relations. This task is particularly demanding and important for dynamic large-scale scenarios such as those related to border and port security, where suspicious activities need to be detected as the needle in a haystack of largely predominant "pattern of life" activities involving many entities and actors. In this chapter, we highlight the capabilities of the recent Statistical Relational Learning framework of Markov Logic Networks for SA in maritime scenarios, and provide some examples and practical advice on their use, further detailing our previous work.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11390/1107015
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