摘要
多智能体系统能够在部分能力不足时更有效地到达目标,具有许多组件的多智能体系统如何到达期望目标是一个重要议题。受生物的嗅觉和视觉导航模式启发,使用群决策方法建立多智能体导航模型,以更好地发挥多智能体系统鲁棒性优势。由于多个智能体对信息和动作的集体决策,驱使模型生物朝着特定目标前进。使用栅格地图开发受生物嗅觉启发的导航算法,并通过迷宫实验考察使实体尽可能接近目标的最佳参数。实验结果表明,在复杂障碍环境下,在嗅觉和视觉模式之间取得平衡是一个关键点。超出该临界点,模拟生物在复杂环境中的导航能力将大大增加,在智能体能力不足时可越过障碍到达可见目标。
Understanding how multi-agent systems with many components reach a desired target is an important topic.Multi-agent system can reach the goals more effectively when the components are less capability.In order to give full play to its robustness,inspired by the olfacto⁃ry and visual navigation modes of organisms,this paper uses group decision-making method to build a multi-agent navigation model.The com⁃plexity caused by multi agents’collective feedback on information and actions drives the model organisms toward to the goal.The grid map is used to achieve the navigation algorithm based on biological olfactory heuristic,and the best parameters to make the entity as close as possible to the target are investigated in the environment with maze.And the experimental results show that achieving a balance between the sense of smell and vision is a critical point.Beyond this critical point,the possibility of an entity looking for food in a complex environment will greatly increase,it can cross obstacles to reach visible targets when the ability of agent is insufficient.
作者
刘鹏元
苏先创
张惠凯
LIU Peng-yuan;SU Xian-chuang;ZHANG Hui-kai(School of Information,Zhejiang Sci-Tech University,Hangzhou 310018,China)
出处
《软件导刊》
2022年第4期73-78,共6页
Software Guide
基金
国家自然科学基金项目(62003307)。
关键词
多智能体系统
分散式系统
群决策办法
生物启发模型
群体智能
multi-agent system
decentralized system
group decision-making
biologically inspired model
swarm intelligence
作者简介
刘鹏元(1996-),女,浙江理工大学信息学院硕士研究生,研究方向为多智能体系统;通讯作者:苏先创(1980-),男,博士,浙江理工大学信息学院讲师,研究方向为人工智能、生物信息学;张惠凯(1999-),男,浙江理工大学信息学院学生,研究方向为人工智能。
