Abstract- A decision making mechanism based on Hidden Markov Models (HMMs) was presented in the paper. This paper is concerned with the modelling of the behaviour of players operating in a competitive environment that is characterized by interactions amongst players or groups of players. Thus a new, on-line, hierarchical, probabilistic modelling architecture with a probabilistic decision tree was developed for the purpose of on-line behaviour recognition that accepts HMM behaviour probabilities of player and effectively segments their behaviour-with-time trajectories. This allows the location of important points in time where behaviour changes occur. Furthermore, the hierarchical nature of the system allows individual player classification results to be used towards the modelling and classification of higher-level tactical behaviours of groups of players, as defined within an application envelope. The system is applied in a relatively simple 2-D “air patrol” scenario and system simulation performance results are provided in terms of certain useful metrics.
[1]
Lawrence R. Rabiner,et al.
A tutorial on hidden Markov models and selected applications in speech recognition
,
1989,
Proc. IEEE.
[2]
Zoubin Ghahramani,et al.
An Introduction to Hidden Markov Models and Bayesian Networks
,
2001,
Int. J. Pattern Recognit. Artif. Intell..
[3]
Yoshua Bengio,et al.
Markovian Models for Sequential Data
,
2004
.
[4]
Robert L. Shaw,et al.
Fighter Combat: Tactics and Maneuvering
,
1985
.
[5]
Paul D. Gader,et al.
Generalized hidden Markov models. I. Theoretical frameworks
,
2000,
IEEE Trans. Fuzzy Syst..
[6]
Lawrence R. Rabiner,et al.
A tutorial on Hidden Markov Models
,
1986
.
[7]
Ronald D. Chaney,et al.
Hidden Markov models for threat prediction fusion
,
2000,
SPIE Defense + Commercial Sensing.