Early Classification of Intent for Maritime Domains Using Multinomial Hidden Markov Models

The need for increased maritime security has prompted research focus on intent recognition solutions for the naval domain. We consider the problem of early classification of the hostile behavior of agents in a dynamic maritime domain and propose our solution using multinomial hidden Markov models (H...

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Bibliographic Details
Published in:Frontiers in artificial intelligence 2021-10, Vol.4, p.702153-702153
Main Authors: Carlson, Logan, Navalta, Dalton, Nicolescu, Monica, Nicolescu, Mircea, Woodward, Gail
Format: Article
Language:English
Subjects:
Artificial Intelligence
behavior recognition
intent recognition
machine learning
maritime
multinomial HMM
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Summary:The need for increased maritime security has prompted research focus on intent recognition solutions for the naval domain. We consider the problem of early classification of the hostile behavior of agents in a dynamic maritime domain and propose our solution using multinomial hidden Markov models (HMMs). Our contribution stems from a novel encoding of observable symbols as the rate of change (instead of static values) for parameters relevant to the task, which enables the early classification of hostile behaviors, well before the behavior has been finalized. We discuss our implementation of a one-versus-all intent classifier using multinomial HMMs and present the performance of our system for three types of hostile behaviors (ram, herd, block) and a benign behavior.
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2021.702153