NDLI: Maritime threat detection using plan recognition

Content Provider	IEEE Xplore Digital Library
Author	Auslander, B. Gupta, K.M. Aha, D.W.
Copyright Year	2012
Description	Author affiliation: Knexus Research Corp.; 163 Waterfront Street; National Harbor, MD 20745, USA (Auslander, B.; Gupta, K.M.) \|\| Navy Center for Applied Research in Artificial Intelligence; Naval Research Laboratory, Code 5514; 4555 Overlook Ave. SW; Washington, DC 20375, USA (Aha, D.W.)
Abstract	Existing algorithms for maritime threat detection employ a variety of normalcy models that are probabilistic and/or rule-based. Unfortunately, they can be limited in their ability to model the subtlety and complexity of multiple vessel types and their spatio-temporal events, yet their representation is needed to accurately detect anomalies in maritime scenarios. To address these limitations, we apply plan recognition algorithms for maritime anomaly detection. In particular, we examine hierarchical task network (HTN) and case-based algorithms for plan recognition, which detect anomalies by generating expected behaviors for use as a basis for threat detection. We compare their performance with a behavior recognition algorithm on simulated riverine maritime traffic. On a set of simulated maritime scenarios, these plan recognition algorithms outperformed the behavior recognition algorithm, except for one reactive behavior task in which the inverse occurred. Furthermore, our case-based plan recognizer outperformed our HTN algorithm. On the short-term reactive planning scenarios, the plan recognition algorithms outperformed the behavior recognition algorithm on routine plan following. However, they are significantly outperformed on the anomalous scenarios.
Starting Page	249
Ending Page	254
File Size	1082906
Page Count	6
File Format	PDF
ISBN	9781467327084
e-ISBN	9781467327091
DOI	10.1109/THS.2012.6459857
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Hidden Markov models Probabilistic logic Prediction algorithms Planning Labeling Collision avoidance
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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