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http://dx.doi.org/10.7583/JKGS.2016.16.2.27

Stealthy Behavior Simulations Based on Cognitive Data  

Choi, Taeyeong (School of Computing, Informatics, Decision Systems Engineering, Arizona State University)
Na, Hyeon-Suk (School of Computer Science and Engineering, Soongsil University)
Publication Information
Journal of Korea Game Society / v.16, no.2, 2016 , pp. 27-40 More about this Journal
Abstract
Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.
Keywords
Reinforcement learning; Artificial neural network; Game level design; Game simulation;
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