DOI QR코드

DOI QR Code

A Model of Pursuing Energy of Predator in Single Predator-Prey Environment

단일 포식자-희생자 환경에서 포식자 추격 에너지 모델

  • Lee, Jae Moon (Dept. of Multimedia Engineering, Hansung University) ;
  • Kwon, Young Mee (Dept. of Multimedia Engineering, Hansung University)
  • 이재문 (한성대학교 멀티미디어공학과) ;
  • 권영미 (한성대학교 멀티미디어공학과)
  • Received : 2013.01.03
  • Accepted : 2013.01.28
  • Published : 2013.02.20

Abstract

In general, the predator-prey model has been studied as a model of struggle for existence in a ecosystem. While conventional papers have focussed on the population change of the predator-prey, this paper focused on controlling the energy needed for the predator to pursue the prey. For simplification, assume the environment which there are only single predator and prey. Based on the environment, a certain amount of energy needed for a predator to pursue the prey was suggested on a basis of physical theories and also the used energy model was suggested on a basis of the simulation. From experiments, it was proven that the suggested energy models were appropriate for natural pursuit.

일반적으로 생태계에서 포식자-희생자 모델은 생존 경쟁의 연구모델로서 많이 연구되어 왔다. 기존의 논문이 포식자-희생자의 개체 수 변화량에 초점을 맞추고 있는 반면, 본 논문은 포식자-희생자 모델에서 포식자가 희생자를 추격하기에 필요한 에너지 제어에 관한 연구를 하였다. 문제를 간단히 하기 위하여 한 마리의 포식자와 한 마리의 희생자가 있다고 가정하였고, 이를 기반으로 일정한 거리에 있는 포식자가 희생자를 추격하여 성공하기에 필요 에너지를 물리적 이론을 근거로 제시하였고, 시뮬레이션에 기반하여 소비 에너지 모델을 제안하였다. 실험을 통하여 제안된 두 에너지 모델이 자연스러운 추격하기에 올바르게 적용될 수 있음을 보였다.

Keywords

References

  1. Reynolds, C. W., "Flocks, Herds, and Schools: A Distributed Behavioral Model", SIGGRAPH, 21(4), pp. 25-34, 1987.
  2. Mat Buckland, "Programming Game AI by Example", ISBN 1556220782, Wordware Publications, 2005.
  3. Gianluigi Folino, Agostino Forestiero, Giandomenico Spezzano, "An adaptive flocking algorithm for performing approximate clustering", Inf. Sci., 179(18), pp. 3059-3078, 2009. https://doi.org/10.1016/j.ins.2009.05.017
  4. Jae Moon Lee, "An Efficient Algorithm to Find k-Nearest Neighbors in Flocking Behavior", Information Processing Letters, pp. 576-579, 2010.
  5. Xiaoyuan Luo, Shaobao Li, Xinping Guan, "Flocking algorithm with multi-target tracking for multi-agent systems", Pattern Recognition Letters, pp. 800-805, 31, 2010. https://doi.org/10.1016/j.patrec.2010.01.014
  6. Vladimir Zhdankin and J. C. Sprott, "Simple predator-prey swarming model", PHYSICAL REVIEW E 82, 056209-1-7, 2010. https://doi.org/10.1103/PhysRevE.82.056209
  7. Dal-ho Cho, Yong ho Lee, Jin Hyung Kim, So-Young Park and Dae-Woong Rhee, "NPC Control Model for Defense in Soccer Game Applying the Decision Tree Learning Algorithm", Journal of Korea Game Society, v.11, no.6, pp.61-70, 2011.
  8. Sung Hyun Cho and Jae Moon Lee, "Group Behavior Simulation of Multi-Agents by Using Steering Forces in an Enclosed Space", Journal of Korea Game Society, v.11, no.1, pp.111-120, 2011.

Cited by

  1. Design and Prototype Development of An Agent for Self-Driving Car vol.15, pp.5, 2015, https://doi.org/10.7583/JKGS.2015.15.5.131
  2. Implementation of Natural Behavior Patterns of Monster based on Energy Model vol.14, pp.5, 2014, https://doi.org/10.7583/JKGS.2014.14.5.87
  3. Analysis of Behaviour of Prey to avoid Pursuit using Quick Rotation vol.13, pp.6, 2013, https://doi.org/10.7583/JKGS.2013.13.6.27
  4. Comparison of Algorithms to find Continuous k-nearest Neighbors to be Appropriate under Gaming Environments vol.13, pp.3, 2013, https://doi.org/10.7583/JKGS.2013.13.3.47