Chiao, S.-Y. and Xydeas, C.S. (2004) Behavior modeling using a hierarchical HMM approach. In: Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on. IEEE, pp. 92-97. ISBN 0-7695-2291-2Full text not available from this repository.
We introduce a new methodology for the hierarchical modeling of the behavior-with-time of players operating and interacting within a certain application domain. Behavior modelling and characterization are performed online, given that a number of observations are made or sensed at regular time intervals with respect to each player. A key element of this hierarchical behavior modeling system architecture is a new formulation of multiple hidden Markov models (HMM) with discrete densities operating in parallel, with each HMM accepting a single feature-related observation sequence. However the proposed classification approach recognizes the existence of possible dependencies between the observation sequences of the features obtained for a given player. This property is effectively exploited in a new dependent-multiHMM with discrete densities (DM-HMM-D) classification approach. The proposed methodology is applied in modeling the behavior of aircrafts operating in relatively simple 3D "air-patrol" situations. Computer simulation results demonstrate the significant gains that can be obtained in system classification and modeling performance when compared to those obtained while using conventional independent-multidiscrete hidden Markov model (IM-HMM-D) schemes.
|Item Type:||Contribution in Book/Report/Proceedings|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||12 Oct 2012 13:41|
|Last Modified:||09 Dec 2016 00:09|
Actions (login required)