Stealthy Behavior Simulations Based on Cognitive Data

인지 데이터 기반의 스텔스 행동 시뮬레이션

  • Choi, Taeyeong (School of Computing, Informatics, Decision Systems Engineering, Arizona State University) ;
  • Na, Hyeon-Suk (School of Computer Science and Engineering, Soongsil University)
  • 최태영 (애리조나주립대학교 컴퓨터공학과) ;
  • 나현숙 (숭실대학교 컴퓨터학부)
  • Received : 2016.03.03
  • Accepted : 2016.04.15
  • Published : 2016.04.20


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.

스텔스 게임에서 플레이어의 행동을 예측하는 것은 게임 디자인에 있어서 핵심적인 역할을 한다. 하지만, 플레이어와 게임 환경 간의 상호작용이 실시간으로 일어난다는 점에서 이러한 예측 프로세스를 자동화하는 것은 어려운 문제이다. 본 논문은 동적 환경에서의 스텔스 움직임을 예측하기 위한 강화학습 방법을 소개하며, 이를 위해 Q-learning과 인공신경망이 통합된 형태의 모델이 액션 시뮬레이션을 위한 분류기로 활용된다. 실험 결과들은 이러한 시뮬레이션 에이전트가 동적으로 변하는 주변 상황에 민감하게 반응함을 보여주며, 따라서 게임 레벨 디자이너가 다양한 게임 요소들을 결정하는데 유용함을 보여준다.



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