Recognising Agent Behaviour During Variable Length Activities

Authors:
Rolf Baxter;David Lane;Yvan Petillot
Affiliations:
Ocean Systems Laboratory, Heriot-Watt University, UK, email: {R.Baxter,D.M.Lane,Y.R.Petillot}@hw.ac.uk;Ocean Systems Laboratory, Heriot-Watt University, UK, email: {R.Baxter,D.M.Lane,Y.R.Petillot}@hw.ac.uk;Ocean Systems Laboratory, Heriot-Watt University, UK, email: {R.Baxter,D.M.Lane,Y.R.Petillot}@hw.ac.uk
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

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.