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Modeling the behaviors of players in competitive environments

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Modeling the behaviors of players in competitive environments. / Chiao, Shih-Yang; Xydeas, C.S.
Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on. IEEE, 2003. p. 566-569.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Chiao, S-Y & Xydeas, CS 2003, Modeling the behaviors of players in competitive environments. in Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on. IEEE, pp. 566-569. https://doi.org/10.1109/IAT.2003.1241146

APA

Chiao, S.-Y., & Xydeas, C. S. (2003). Modeling the behaviors of players in competitive environments. In Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on (pp. 566-569). IEEE. https://doi.org/10.1109/IAT.2003.1241146

Vancouver

Chiao SY, Xydeas CS. Modeling the behaviors of players in competitive environments. In Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on. IEEE. 2003. p. 566-569 doi: 10.1109/IAT.2003.1241146

Author

Chiao, Shih-Yang ; Xydeas, C.S. / Modeling the behaviors of players in competitive environments. Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on. IEEE, 2003. pp. 566-569

Bibtex

@inproceedings{a5f7416745214f4f8796f89ecaf07510,
title = "Modeling the behaviors of players in competitive environments",
abstract = "This paper is concerned with the modeling of the behavior of players operating in a competitive environment that is characterized by interactions amongst players or groups of players. Thus the paper describes a new, online, hierarchical, probabilistic modeling architecture that is based on hidden Markov models (HMMs). For the purpose of online behavior recognition, a probabilistic decision tree is implemented that accepts HMM behavior probabilities of player and effectively segments their behavior-with-time trajectories. This allows the location of important points in time where behavior changes occur. Furthermore, the hierarchical nature of the system allows individual player classification results to be used towards the modeling and classification of higher-level tactical behaviors of groups of players, as defined within an application envelope. The system is applied in a relatively simple {"}air-patrol{"} scenario and system simulation performance results are provided in terms of certain useful metrics.",
author = "Shih-Yang Chiao and C.S. Xydeas",
year = "2003",
month = oct,
day = "1",
doi = "10.1109/IAT.2003.1241146",
language = "English",
isbn = "0-7695-1931-8",
pages = "566--569",
booktitle = "Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Modeling the behaviors of players in competitive environments

AU - Chiao, Shih-Yang

AU - Xydeas, C.S.

PY - 2003/10/1

Y1 - 2003/10/1

N2 - This paper is concerned with the modeling of the behavior of players operating in a competitive environment that is characterized by interactions amongst players or groups of players. Thus the paper describes a new, online, hierarchical, probabilistic modeling architecture that is based on hidden Markov models (HMMs). For the purpose of online behavior recognition, a probabilistic decision tree is implemented that accepts HMM behavior probabilities of player and effectively segments their behavior-with-time trajectories. This allows the location of important points in time where behavior changes occur. Furthermore, the hierarchical nature of the system allows individual player classification results to be used towards the modeling and classification of higher-level tactical behaviors of groups of players, as defined within an application envelope. The system is applied in a relatively simple "air-patrol" scenario and system simulation performance results are provided in terms of certain useful metrics.

AB - This paper is concerned with the modeling of the behavior of players operating in a competitive environment that is characterized by interactions amongst players or groups of players. Thus the paper describes a new, online, hierarchical, probabilistic modeling architecture that is based on hidden Markov models (HMMs). For the purpose of online behavior recognition, a probabilistic decision tree is implemented that accepts HMM behavior probabilities of player and effectively segments their behavior-with-time trajectories. This allows the location of important points in time where behavior changes occur. Furthermore, the hierarchical nature of the system allows individual player classification results to be used towards the modeling and classification of higher-level tactical behaviors of groups of players, as defined within an application envelope. The system is applied in a relatively simple "air-patrol" scenario and system simulation performance results are provided in terms of certain useful metrics.

U2 - 10.1109/IAT.2003.1241146

DO - 10.1109/IAT.2003.1241146

M3 - Conference contribution/Paper

SN - 0-7695-1931-8

SP - 566

EP - 569

BT - Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on

PB - IEEE

ER -