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A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games
Joint Authors
Jiao, Peng
Sun, Lin
Xu, Kai
Yue, Shiguang
Wei, Xiangyu
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-16
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Multiagent goal recognition is a tough yet important problem in many real time strategy games or simulation systems.
Traditional modeling methods either are in great demand of detailed agents’ domain knowledge and training dataset for policy estimation or lack clear definition of action duration.
To solve the above problems, we propose a novel Dec-POMDM-T model, combining the classic Dec-POMDP, an observation model for recognizer, joint goal with its termination indicator, and time duration variables for actions with action termination variables.
In this paper, a model-free algorithm named cooperative colearning based on Sarsa is used.
Considering that Dec-POMDM-T usually encounters multiagent goal recognition problems with different sorts of noises, partially missing data, and unknown action durations, the paper exploits the SIS PF with resampling for inference under the dynamic Bayesian network structure of Dec-POMDM-T.
In experiments, a modified predator-prey scenario is adopted to study multiagent joint goal recognition problem, which is the recognition of the joint target shared among cooperative predators.
Experiment results show that (a) Dec-POMDM-T works effectively in multiagent goal recognition and adapts well to dynamic changing goals within agent group; (b) Dec-POMDM-T outperforms traditional Dec-MDP-based methods in terms of precision, recall, and F-measure.
American Psychological Association (APA)
Jiao, Peng& Xu, Kai& Yue, Shiguang& Wei, Xiangyu& Sun, Lin. 2017. A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games. Discrete Dynamics in Nature and Society،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1151446
Modern Language Association (MLA)
Jiao, Peng…[et al.]. A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games. Discrete Dynamics in Nature and Society No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1151446
American Medical Association (AMA)
Jiao, Peng& Xu, Kai& Yue, Shiguang& Wei, Xiangyu& Sun, Lin. A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games. Discrete Dynamics in Nature and Society. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1151446
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1151446