人工鱼群高级自组织行为研究

Research on Advanced Self-organization Behavior for Artificial Fish School

在Tu Xiaoyuan和John David Funge研究工作的基础上,进一步研究人工鱼群的高级自组织行为.基于个体人工鱼的行为模型,提出一种基于认知的人工鱼群高级行为自组织方法.该方法中,每条人工鱼被看作一个agent.通过感知外部虚拟环境信息,agent产生行为意图.人工鱼群的自组织行为通过多个agent间的相互作用涌现形成,如人工鱼群的运动、捕食、逃逸等行为规划,从而体现自然鱼群的生物特性,实现对自然鱼群高级行为的逼真模拟.我们设计和实现的基于认知的人工鱼群动画系统,测试验证了所提出的高级行为自组织疗法的有效性.

Based on research work of Tu and Funge, the advanced self-organization behaviors of fish school were further researched. An self-organization approach of advanced behavior for artificial fish school was presented based on cognition. In this approach, each artificial fish was regarded as an agent, the self- organization behaviors of artificial fish school were emerged from the interactions among many agents, such as behavior planning of motion, predation and escaping. The biologic characteristics of natural fish school were shown, and the advanced behaviors of natural fish school were simulated. A cognition-based artificial fish school animation system was tested efficaciously using the approach of the advanced self-organization in this paper.