Open AccessIdentifying Terrorist Activity with AI Plan‐Recognition Technology
Author(s) -
Jarvis Peter A.,
Lunt Teresa F.,
Myers Karen L.
Publication year - 2004
Publication title -
ai magazine
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
ISBN - 0-262-51183-5
DOI - 10.1609/aimag.v26i3.1827
Subject(s) - computer science , plan (archaeology) , process (computing) , cardinality (data modeling) , presentation (obstetrics) , heuristic , terrorism , computer security , work (physics) , data science , artificial intelligence , data mining , engineering , medicine , mechanical engineering , archaeology , radiology , history , operating system
We describe the application of plan‐recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data‐mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application. In particular, we outline the need to bring the analysts into the process and for heuristic improvements to the plan‐recognition algorithm.