A Dynamic Hierarchical Evaluating Network for Real-Time Strategy Games

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.