Formal Framework for Analysis of Adaptation in Multi-Agent Systems (ADAPT2)

多智能体系统适应分析的正式框架 (ADAPT2)

• 批准号: 0535182
• 负责人: Kristina Lerman
• 金额: $ 26.8万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-15 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535182&HistoricalAwards=false
• 关键词: Formal; Framework; Analysis; Adaptation; Multi

This research will develop a general framework for mathematical analysis of collective behavior of adaptive multi-agent systems. Adaptation is an essential requirement for autonomous multi-agent systems functioning in uncertain dynamic environments, for example, distributed robot teams, modules in an embedded system, nodes in a sensor network, or software agents. Adaptation allows agents to change their behavior in response to changes in the environment or actions of other agents. Mathematical analysis of adaptive systems will enable researchers to design more robust systems, and to predict, control and understand their behavior. The research will study agents that make decisions autonomously based on local information, which comes either from interactions with other agents or from the local environment. In particular, this project will examine different classes of adaptive behavior, such as adaptation through reinforcement and adaptation through communication via spatially extended fields. Reinforcement learning is a powerful framework where an agent learns optimal actions through a trial and error exploration of the environment and by receiving rewards for good actions. Collective adaptation can also take place in systems in which agents are coupled through external fields, for example, through markers they deposit in the environment. Although adaptation and learning have long been the focus of the artificial intelligence community, there is relatively little work examining how a group of adaptive agents will act. The difficulty arises from the fact that agents adapt in the presence of other adaptive agents. Often it is not a priori clear how the system will act or even if adaptation will achieve the desired goals. In addition, the designer has very little guidance about what individual agent characteristics are required to guarantee the desired collective behavior. The lack of a formal understanding of these problems has prevented researchers from taking full advantage of this powerful design paradigm. The mathematical analysis to be performed in this research will help answer these questions. There is a critical need for better foundations and tools for analyzing multi-agent behavior and verifying control mechanisms for multi-agent systems. The lack of such tools stands in the way of wider deployment of such systems, especially robots and embedded systems. Experiments and simulations that are necessary to validate control algorithms are time consuming and costly. Quantitative understanding provided by the mathematical models to be developed in this project will lead to more robust and efficient control algorithms and greater deployment of such systems in the field.

这项研究将为自适应多智能体系统的集体行为的数学分析开发一个通用框架。适应是在不确定的动态环境中运行的自主多智能体系统的基本要求，例如分布式机器人团队、嵌入式系统中的模块、传感器网络中的节点或软件代理。适应允许代理改变其行为以响应环境的变化或其他代理的行为。自适应系统的数学分析将使研究人员能够设计更强大的系统，并预测、控制和理解它们的行为。该研究将研究根据本地信息自主做出决策的代理，这些信息要么来自与其他代理的交互，要么来自本地环境。特别是，该项目将研究不同类别的适应行为，例如通过强化的适应和通过空间扩展场进行交流的适应。强化学习是一个强大的框架，代理通过对环境的试错探索并通过良好行动获得奖励来学习最佳行动。集体适应也可以发生在通过外部场耦合的系统中，例如通过它们在环境中沉积的标记。尽管适应和学习长期以来一直是人工智能界的焦点，但研究一组自适应智能体如何行动的工作相对较少。困难来自于代理在其他自适应代理存在的情况下进行适应的事实。通常，我们并不清楚系统将如何运作，甚至适应是否会实现预期目标。此外，设计者对于需要哪些个体代理特征来保证所需的集体行为几乎没有指导。由于缺乏对这些问题的正式理解，研究人员无法充分利用这种强大的设计范式。本研究中进行的数学分析将有助于回答这些问题。迫切需要更好的基础和工具来分析多智能体行为和验证多智能体系统的控制机制。缺乏此类工具阻碍了此类系统的更广泛部署，尤其是机器人和嵌入式系统。验证控制算法所需的实验和模拟既耗时又昂贵。该项目中开发的数学模型提供的定量理解将导致更强大和更高效的控制算法以及此类系统在现场的更大部署。