CAREER: Scalable Algorithms for Individual Decision Making in Multiagent Settings

职业：多智能体环境中个人决策的可扩展算法

• 批准号: 0845036
• 负责人: Prashant Doshi
• 金额: $ 42.97万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845036&HistoricalAwards=false
• 关键词: CAREER; Scalable; Algorithms; Individual; Decision

Research under this award is developing efficient and effective methods for strategic decision making by an individual artificial agent cohabiting with other agents in uncertain environments. For example, how should an autonomous unmanned aerial vehicle decide between closer surveillance of a possible fugitive or intercepting the target who may be aware of the monitoring? Toward this goal, the research is identifying the sources of computational complexity and understanding the conflicting interrelationship between computational efficiency and decision-making effectiveness. This problem of individual decision making in uncertain multiagent settings is formalized using a recognized framework that combines the decision-theoretic paradigm of partially observable Markov decision processes (POMDPs) with elements of Bayesian games and interactive epistemology. In this framework, called interactive POMDP (I-POMDP), the research utilizes innovative ways of minimally modeling contextual knowledge in multiagent settings, exploits novel decision-making heuristics and embedded structure in problems.Integration of research and education is manifest in the development and delivery of a multi-disciplinary course on strategic decision making under uncertainty, which integrates and compares normative theories with real human decision-making behavior.By combining aspects of decision and game theories, both of which seek to understand normative ways of decision making, with attention to real human decision-making behavior, this research is contributing to long-term research and development of artificial agents that can assist with rational, long-term decision making and planning in areas including emergency response, environmental sustainability, autonomous vehicles and many others.

该奖项下的研究正在开发一个人工智能体与其他智能体在不确定环境中共同生活的有效和有效的战略决策方法。例如，自主无人驾驶飞行器应该如何决定是对可能的逃犯进行更密切的监视，还是拦截可能知道监视的目标？为了实现这一目标，本研究确定了计算复杂性的来源，并理解了计算效率和决策有效性之间相互冲突的关系。这个问题的个人决策在不确定的多智能体设置正式使用公认的框架，结合部分可观察马尔可夫决策过程（POMDPs）的决策理论范式与贝叶斯游戏和交互式认识论的元素。在这个框架中，被称为交互式POMDP（I-POMDP），研究利用创新的方法，最低限度地建模上下文知识在多智能体设置，开发新的决策知识和嵌入结构的问题。研究和教育的整合是体现在开发和交付的多学科课程的战略决策下的不确定性，它将规范理论与真实的人类决策行为进行整合和比较。通过将决策和博弈论的各个方面结合起来，两者都试图理解决策的规范方式，关注真实的人类决策行为，这项研究有助于人工代理的长期研究和开发，这些代理可以在包括应急响应、环境可持续性、自动驾驶汽车和许多其他领域协助进行合理的长期决策和规划。