DOI QR코드

DOI QR Code

An Artificial Intelligence Evaluation on FSM-Based Game NPC

FSM 기반의 게임 NPC 인공 지능 평가

  • Lee, MyounJae (Division of Information & Communication, BaekSeok University)
  • 이면재 (백석대학교 정보통신학부)
  • Received : 2014.09.06
  • Accepted : 2014.10.07
  • Published : 2014.10.20

Abstract

NPC in game is an important factor to increase the fun of the game by cooperating with player or confrontation with player. NPC's behavior patterns in the previous games are limited. Also, there is not much difference in NPC's ability among the existing games because it's designed to FSM. Therefore, players who have matched with NPCs which have the characteristics may have difficulty to play. This paper is for improving the problem and production and evaluation of the game NPC behavior model based on wolves hunting model in real life. To achieve it, first, the research surveys and studies behavior states for wolves to capture prey in the real world. Secondly, it is implemented using the Unity3D engine. Third, this paper compares the implemented state transition probability to state transition probability in real world, state transition probability in general game. The comparison shows that the number of state transitions of NPCs increases, proportions of implemented NPC behavior patterns converges to probabilities of state transition in real-world. This means that the aggressive behavior pattern of NPC implemented is similar to the wolf hunting behavior pattern of the real world, and it can thereby provide more player experience.

게임 NPC(Non Player Character)는 게임 플레이어와 대전 또는 협력함으로써 게임의 재미를 증가시키는 중요한 요소이다. 대부분 기존 게임에서 제공되는 NPC 인공지능은 FSM(Finite State Machine)으로 제작되어 행동 패턴이 정해져 있고 능력이 동일한 특징을 갖고 있다. 따라서 이러한 특징을 갖는 NPC들과 대전하는 플레이어는 창조적인 게임 플레이를 진행하는 것이 어려울 수 있다. 본 논문은 이 문제점을 개선하기 위하여 실제 생활에서 늑대들이 먹이를 사냥하는 행동 모델을 게임 NPC의 행동 모델로 제작하고 이를 평가하기 위한 것이다. 이를 위하여 첫째, 실세계에서 늑대들이 먹이를 포획하기 위한 행동 상태들을 조사 연구한다. 둘째, 이 행동 상태들을 Unity3D 엔진을 이용하여 구현한다. 셋째, 구현된 NPC들의 상태 전이 비율과 실세계의 NPC들의 상태 전이 비율, 일반적인 게임 NPC의 상태 전이 비율을 비교한다. 비교 결과, 구현된 NPC들의 상태 전이 비율은 실세계의 상태 전이 비율과 비슷함을 보인다. 이는 구현된 NPC들의 행동 패턴이 실세계의 늑대 사냥 행동 패턴과 유사함을 의미하는데, 이렇게 함으로써 플레이어에게 보다 증가된 사용자 경험을 제공할 수 있다.

Keywords

References

  1. Lee Eun-Hee, Park Choong-Shik, Cho Sung-Hyun, "A Study on the Intelligent NPC in MMORPG", Proceedings of Korea Contents Society, Vol.4, No.1, 2006.
  2. Manjae Lee, "Artificial Intelligence in Game", Korea Information Processing Society, Vol.9, No.3,pp.69-76, 2002.5
  3. N.D Cho, B.G Sung, K.T Kim, "Artificial life simulation game characters through the implementation of the strategy", Spring Conference of KISSE, Vol.27, No.1, pp.241-243, 2000.
  4. David B. Fogel, "Using Evolutionary Programming to Create Neural Networks That are Capable of Playing Tic-Tac-Toe", Int'l Joint Conf. Neural Networks, New York, pp.875-880, 1993.
  5. Marc Ponsen, IMPROVING ADAPTIVE GAME AI WITH EVOLUTIONARY LEARNING, a thesis submitted in fulfillment of the requirements for the degree of Master of Science, Delft, 2004.
  6. Ross Graham, Hugh McCabe and Stephen Sheridan, "Path finding in Computer Games", ITB J., Vol. 9, pp.223-230, 2004.
  7. Incheol Kim, "Utilizing Computer Games for Effective AI Education, Korean Society For Computer Game", Vol. 26, No. 3, pp.109-118, 2014.
  8. MyounJae Lee, "Implementation of NPC Artificial Intelligence Using Agonistic Behavior of Animals", Journal of digital convergence, Vol.12, No.1, 2014.
  9. Myoung-hee Cha, "A Study for Autonomous Learning capacity for plan-based NPC", Korean Society For Computer Game, Volume.25, No.3, pp.149-156, 2012.
  10. Jae Moon Lee, "An Efficient Flocking Behaviors for Large Flocks by Using Representative Boid", Journal of Game Society, Vol.8, no. 3, pp.87-95, 2008.
  11. J. D. Madden, R.C. Arkin, D. MacNulty, "Multi-robot System Based on Model of Wolf Hunting Behavior to Emulate Wolf and Elk Interactions", Proc. IEEE International Conference on Robotics and Biomimetics (ROBIO 2010), Tianjin, China, Dec. 2010.
  12. D.R. MacNulty, L.D. Mech, D.W. Smith, "A Proposed Ethogram of Large-carnivore Predatory Behavior, Exemplified by the Wolf", Journal of Mammalogy, Vol. 88, No. 3, pp.595-605, June 2007. https://doi.org/10.1644/06-MAMM-A-119R1.1
  13. H.S Park, K.J Kim, "Current research of artificial intelligence games", KISSE, 2013.7
  14. A. S'anchez and A. Weitzenfeld, "Multi- Agent Formations in a Herd of Wolves Hunting Model", Technical report, Instituto Tecnol'ogico Autonomo de M'exico, Mexico City, Mexico 2004.
  15. Nara Kim, Kyhyun Um, Kyungeun Cho, "An Action Information Management Method for Creating Adaptive NPC", Journal of Game Society, Vol.8, No.3, 2008.3.
  16. Kyungeun Cho, Hyungje Cho, "Generating various NPCs Behavior using Inference of Stochastic Finite Automata", Journal of Game Society, Vol.2, No.2, 2002.11.