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一种适合网络游戏的多NPC协同运动策略 被引量:2

NPC Cooperative Motion Strategy Fit for Online Games
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摘要 研究群体非玩家控制角色(NPC)如何根据具体游戏环境和剧情因素进行相互联系的协同运动以实现群体行为。在此基础上,提出一种适合网络游戏的多NPC协同运动策略,采用人工势场法描述游戏状态,并利用粒子群算法确定下一时刻每个NPC的最佳运动位置,从而实现个体NPC根据游戏状态自主完成群体作战目标。实验结果表明,该方法能够在网络游戏中保证群体NPC高效、自主地适应环境,并能较好地完成与玩家对抗的游戏任务。 For the Non-Player Character(NPC) cooperative motion which realizes the swarm behaves model in conformity to the states and scenarios of games,this paper presents a cooperative motion strategy of multi-NPC for online games,which describes games states by artificial potential field and computes the next best position of NPC by PSO.It realizes the self-determination motion of individual NPC based on the goal of collective campaign.Experimental results show that the presented method can assure that NPC confronts with the players effectively and independently in online games.
作者 毕静
出处 《计算机工程》 CAS CSCD 北大核心 2011年第7期181-183,186,共4页 Computer Engineering
基金 沈阳市科学技术计划基金资助项目"大规模网络游戏中协同技术的研究"(1091185-1-00) 中航一集团航空科学基金资助项目"大规模分布式协同空战仿真系统的研究"(2008ZC54008)
关键词 协同运动 粒子群优化 人工势场 网络游戏 cooperative motion PSO artificial potential field online games
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参考文献7

  • 1Bayazit O B,Lien J M,Amato N M.Better Group Behaviors in Complex Environments Using Global Roadmaps[C]//Proceedings of the 8th International Conference on Artificial Life.[S.l.]:MIT Press,2002:362-370.
  • 2Mamei M,Zambonelli F.Field-based Motion Coordination in Quake 3 Arena[C]//Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi-agent Systems.New York,USA:ACM Press,2004:1532-1533.
  • 3Chen Yuqing,Zhuang Yan,Wang Wei.Cooperative Control for Formations of Mobile Robots Under the Nonholonomic Constraints[C]//Proceedings of the 6th World Congress on Intelligent Control and Automation.Dalian,China:[s.n.],2006:9042-9046.
  • 4Lindhe M,Ogren P,Johansson K H.Flocking with Obstacle Avoidance:A New Distributed Coordination Algorithm Based on Voronoi Partitions[C]//Proceedings of IEEE International Conference on Robotics and Automation.[S.l.]:IEEE Press,2005:1797-1802.
  • 5Moshtagh N,Jadbabaie A,Daniilidis K.Vision-based Distributed Coordination and Flocking of Multi-agent Systems[C]// Proceedings of Robotics:Science and Systems.Cambridge,USA:[s.n.],2005:2807-2812.
  • 6Yang Yan.A Decentralized and Adaptive Flocking Algorithm for Autonomous Mobile Robots[C]//Proceedings of the International Symposium on Advances in Grid and Pervasive Systems.Kunming,China:[s.n.],2008:262-268.
  • 7李爱国.多粒子群协同优化算法[J].复旦学报(自然科学版),2004,43(5):923-925. 被引量:398

二级参考文献8

  • 1Kennedy J, Eberhart R. Particle swarm optimization [A]. Proc of Int'l Conf on Neural Networks [C]. Piscataway: IEEE Press, 1995. 1942-1948.
  • 2Eberhart R, Kennedy J. A new optimizer using particle swarm theory [A]. Proc of Int'l Symposium on Micro Machine and Human Science [C]. Piscataway: IEEE Service Center, 1995. 39-43.
  • 3Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization [A].In: Furuhashi T,Mckay B,eds. Proc Congress on Evolutionary Computation [C]. Piscataway: IEEE Press, 2001.
  • 4Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations [A]. In: Spector L,eds. Proc of Genetic and Evolutionary Computation Conference [C]. San Fransisco: Morgan Kaufmann Publishers Inc, 2001. 469-476.
  • 5Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments [A]. In: Arabnia H R,eds. Proc of Int'l Conf on Artificial Intelligence [C]. Las Vegas: CSREA Press, 2000. 429-434.
  • 6Parsopoulos K E, Vrahatis M N. Particle swarm optimization method in multiobjective problems [A]. In: Panda B,eds. Proc of ACM Symposium on Applied Computing [C]. Boston: ACM Press, 2002. 603-607.
  • 7Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space [J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73.
  • 8李爱国,覃征,鲍复民,贺升平.粒子群优化算法[J].计算机工程与应用,2002,38(21):1-3. 被引量:303

共引文献397

同被引文献15

  • 1黄向阳,尹怡欣,曾广平,涂序彦.一个基于情感的自主非玩家角色模型[J].计算机工程,2006,32(19):31-33. 被引量:4
  • 2Koenig S, Likhachev M, Furcy D. Lifelong Planning A*[J]. Artificial Intelligence, 2004, 155(1/2): 93-146.
  • 3Lu Yibiao, Huo Xiaoming, Asiotras O, et al. Incremental Multi- scale Search Algorithm for Dynamic Path Planning with Low Worst-case Complexity[J]. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41(6): 1-15.
  • 4Botea A, Müller M. Near Optimal Hierarchical Path-finding[J]. Journal of Game Development, 2004, 1(1): 7-28.
  • 5Sturtevant N. Path Finding Benchmarks[EB/OL]. (2011-05-29). http://www.movingai.com/benchmarks.
  • 6Likhachev M, Ferguson D, Gordon G, et al. Anytime Search in Dynamic Graphs[J]. Artificial Intelligence, 2008, 172(14): 1613- 1643.
  • 7Bulitko V, Lu?trek M, Schaeffer J, et al. Dynamic Control in Real-time Heuristic Search[J]. Artificial Intelligence Research, 2008, 32(10): 419-452.
  • 8Jansen M R, Buro M. HPA* Enhancements[C] //Proc. of American Association for Artificial Intelligence. Edmonton, Alberta, Canada: [s. n.] , 2005: 84-87.
  • 9Kring A, Champandard A J, Samarin N. DHPA* and SHPA*: Efficient Hierarchical Path Finding in Dynamic and Static Game Worlds[C] //Proc. of the 6th Conference on Artificial Intelligence and Interactive Digital Entertainment Conference. [S. l.] : AAAI Publications, 2010: 39-44.
  • 10MAGNENAT-THALMANN N. Modelling socially intelligent virtual humans[A].New York:NY,2009.9.

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