MATEC Web Conf.
Volume 208, 20182018 3rd International Conference on Measurement Instrumentation and Electronics (ICMIE 2018)
|Number of page(s)||6|
|Section||Computer Science and Intelligent Technology|
|Published online||26 September 2018|
A Dynamic Hierarchical Evaluating Network for Real-Time Strategy Games
Department of Modeling and Simulation, National University of Defense Technology, Changsha, China
Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and combat simulation. State evaluation is an important research area for AI planning, which ensures the decision accuracy. Since complex interactions exist among different game aspects, the weighted average model usually cannot be well used to compute the evaluation of game state, which results in misleading player’s generation strategy. In this paper, we take dynamic changes and player’s preference into consideration, analyze player’s preference and units’ relationships base on game theory and propose a dynamic hierarchical evaluating network, denoted as DHEN. Experiments show that the modified evaluating algorithm can effectively improve the accuracy of task planning algorithm for RTS games.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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