Multiagent Search Algorithms for Learning & Planning in Colony-Style Robots

用于学习的多智能体搜索算法

• 批准号: 9109298
• 负责人: Michael Lemmon
• 金额: $ 6万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-01 至 1994-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9109298&HistoricalAwards=false
• 关键词: Multiagent; Search; Algorithms; Learning; &amp

Direct implementation of the sensory-cognitive motor triad for intelligent robotic systems will generally place severe demands on the computing, communication, and memory resources of these systems. One solution to this problem has been to distribute the triad's functionality among various physical components of the robot. The colony-style robot ı1! and its predecessor ı2! (subsummation architecture) represent related methods for achieving this functional distribution. Colony-style robots use a collection(ie., colony) of hierarchically inhibited agents to control the robot. Existing colony-style robots suffer several disadvantages limiting their utility as autonomous system. These disadvantages involve problems in determining the robot's underlying control hierarchy and the inability of existing systems to save and use prior experience. This project uses multiagent processing paradigms called multiagent search strategies of MASS algorithms ı3! to solve the deficiencies of current colony-style systems. MASS algorithms ar inspired by recent work with competitively and cooperatively inhibited neural networks ı3!ı4!. It can be shown, using statistical mechanical arguments, that competitive MASS algorithms can form minimum entropy representations of density functions. This project uses that analytical formalism to develop MASS algorithms capable of learning the components and structure of the colony-style control hierarchy. The research also explores how "cooperative" MASS can be used to realize fully discretized solution of Bellman's dynamic programming equation, and uses this capability to devise a method for implementing long term memory and path planning capabilities in colony-style robots. The project will fully develop, through simulation and analysis, the MASS algorithms needed to realize memory, learning, and planning in colony-style robots. The research will also investigate extensions of this approach to more complex systems such as flexible manufacturing systems.

智能机器人系统的感觉认知运动三元组的直接实现通常会对这些系统的计算、通信和存储资源提出严格的要求。 该问题的一种解决方案是将三元组的功能分布在机器人的各个物理组件之间。 殖民地式机器人ı1！及其前身 ı2! （求和架构）代表实现这种功能分布的相关方法。 群体型机器人使用分层抑制代理的集合（即群体）来控制机器人。 现有的群体式机器人存在一些缺点，限制了它们作为自主系统的实用性。 这些缺点涉及确定机器人底层控制层次的问题以及现有系统无法保存和使用先前的经验。 该项目使用多智能体处理范例，称为 MASS 算法的多智能体搜索策略 ı3！解决当前殖民地式系统的缺陷。 MASS 算法的灵感来自最近有关竞争性和合作性抑制神经网络的研究 ı3!ı4!。 使用统计力学论证可以证明，竞争性 MASS 算法可以形成密度函数的最小熵表示。 该项目使用这种分析形式来开发能够学习群体式控制层次结构的组件和结构的 MASS 算法。 该研究还探索了如何使用“协作”MASS 来实现贝尔曼动态规划方程的完全离散解，并利用这种能力设计出一种在群体式机器人中实现长期记忆和路径规划功能的方法。 该项目将通过仿真和分析，全面开发实现群体型机器人的记忆、学习和规划所需的 MASS 算法。 该研究还将研究这种方法扩展到更复杂的系统，例如柔性制造系统。