Recognising agent behaviour during variable length activities

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In this paper we present a new method for obtaining situation awareness via the automatic recognition of agent behaviours. In contrast to many other approaches, the presented method models different behaviour durations without using a fixed classification window, and does not require a distribution of behaviour durations. We introduce the Variable Window Layered Hidden Markov Model (VW-LHMM) as an extension of the LHMM to specifically address behaviours with irregular duration. We validate our approach by simulating three high-level behaviours within the harbour and coastline security domain. We compare performance against the LHMM and show that our approach provides a 10% improvement in classification accuracy, in addition to earlier classification. © 2010 The authors and IOS Press. All rights reserved.

Original languageEnglish
Title of host publicationECAI 2010
Number of pages6
Publication statusPublished - 2010

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389

Fingerprint Dive into the research topics of 'Recognising agent behaviour during variable length activities'. Together they form a unique fingerprint.

  • Cite this

    Baxter, R., Lane, D., & Petillot, Y. (2010). Recognising agent behaviour during variable length activities. In ECAI 2010 (Vol. 215, pp. 803-808). (Frontiers in Artificial Intelligence and Applications; Vol. 215).