A semi-Markov decision model for recognizing the destination of a maneuvering agent in real time strategy games
type: Item
instance of: scholarly article
title: A semi-Markov decision model for recognizing the destination of a maneuvering agent in real time strategy games
zbMATH Open document ID: 1400.90296
DOI: 10.1155/2016/1907971
author: Quanjun Yin
author: Yabing Zha
author: Peng Jiao
author: Shi-guang Yue
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: Recognizing destinations of a maneuvering agent is important in real time strategy games. Because finding path in an uncertain environment is essentially a sequential decision problem, we can model the maneuvering process by the Markov decision process (MDP). However, the MDP does not define an action duration. In this paper, we propose a novel semi-Markov decision model (SMDM). In the SMDM, the destination is regarded as a hidden state, which affects selection of an action; the action is affiliated with a duration variable, which indicates whether the action is completed. We also exploit a Rao-Blackwellised particle filter (RBPF) for inference under the dynamic Bayesian network structure of the SMDM. In experiments, we simulate agents' maneuvering in a combat field and employ agents' traces to evaluate the performance of our method. The results show that the SMDM outperforms another extension of the MDP in terms of precision, recall, and \(F\)-measure. Destinations are recognized efficiently by our method no matter whether they are changed or not. Additionally, the RBPF infer destinations with smaller variance and less time than the SPF. The average failure rates of the RBPF are lower when the number of particles is not enough.
Mathematics Subject Classification ID: 90C40
Mathematics Subject Classification ID: 91A43
Mathematics Subject Classification ID: 91A15
zbMATH DE Number: 6952865
Wikidata QID: Q59131068
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/1907971
OpenAlex ID: W2236759984
cites work: Q4411146
cites work: Learning hierarchical task network domains from partially observed plan traces
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description: scientific article; zbMATH DE number 6952865