JACIII Vol.21 No.2 pp. 221-227
doi: 10.20965/jaciii.2017.p0221


Battlefield Agent Decision-Making Based on Markov Decision Process

Jia Zhang*,**, Xiang Wang*,**, Fang Deng*,**, Bin Xin*,**, and Wenjie Chen*,**

*School of Automation, Beijing Institute of Technology
Beijing 100081, China

**Key Laboratory of Complex System Intelligent Control and Decision
Beijing 100081, China

June 2, 2016
October 31, 2016
Online released:
March 15, 2017
March 20, 2017
decision support, markov decision process, softmax regression, random forest
Battlefield decision-making is an important part of modern information warfare. It can analyze and integrate battlefield information, reduce operators’ work and assist them to make decisions quickly in complex battlefield environment. The paper presents a dynamic battlefield decision-making method based on Markov Decision Processes (MDP). By this method, operators can get decision support quickly in the case of incomplete information. In order to improve the credibility of decisions, dynamic adaptability and intelligence, softmax regression and random forest are introduced to improve the MDP model. Simulations show that the method is intuitive and practical, and has remarkable advantages in solving the dynamic decision problems under incomplete information.
Cite this article as:
J. Zhang, X. Wang, F. Deng, B. Xin, and W. Chen, “Battlefield Agent Decision-Making Based on Markov Decision Process,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.2, pp. 221-227, 2017.
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