Stealthy Behavior Simulations Based on Cognitive Data인지 데이터 기반의 스텔스 행동 시뮬레이션
- Other Titles
- 인지 데이터 기반의 스텔스 행동 시뮬레이션
- Authors
- 최태영; 나현숙
- Issue Date
- Apr-2016
- Publisher
- 한국게임학회
- Keywords
- Reinforcement learning(강화학습); Artificial neural network(인공신경망); Game level design(게임 레벨 디자인); Game simulation(게임 시뮬레이션).
- Citation
- 한국게임학회 논문지, v.16, no.2, pp.27 - 40
- Journal Title
- 한국게임학회 논문지
- Volume
- 16
- Number
- 2
- Start Page
- 27
- End Page
- 40
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/8069
- ISSN
- 1598-4540
- 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.
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