RI: Small: Understanding Value-based Multiagent Learning and Its Applications

RI：小：了解基于价值的多智能体学习及其应用

• 批准号: 1414935
• 负责人: Michael Littman
• 金额: $ 15.7万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1414935&HistoricalAwards=false
• 关键词: RI; Small; Understanding; Value; based

This project explores the behavior of value-based learning methods in multi-agent environments. Value-based methods make decisions by using experience to estimate the utility impact of alternatives and choosing those with high predicted value. Because they evaluate components of behavior instead of treating behaviors as atomic units, they are computationally and statistically efficient. While these methods have been used in computational experiments for many years, only recently have researchers begun to formally characterize their behavior. Our own preliminary work is finding that some value-based methods exhibit super-Nash behavior, making them particularly worthy of study.More specifically, we are analyzing, mathematically and experimentally, how value-based algorithms perform in several classes of simulated games of varying complexity from the artificial intelligence community, multi-agent engineering applications drawn from the wireless networking area, and as models of human and animal decision making in collaboration with cognitive neuroscientists. Where possible, we are refining existing value-based algorithms to work more efficiently, robustly, and generally than existing algorithms. We are also designing educational outreach activities, including creating entertaining instructional videos on how to promote cooperative behavior in real-life social dilemmas.

该项目探讨了多代理环境中基于价值的学习方法的行为。基于价值的方法通过利用经验来估计替代方案的实用性影响并选择具有高预测价值的方法来做出决策。因为他们评估行为的组成部分，而不是将行为视为原子单位，因此它们在计算和统计上是有效的。尽管这些方法已经在计算实验中使用了很多年，但直到最近才开始正式表征其行为。 Our own preliminary work is finding that some value-based methods exhibit super-Nash behavior, making them particularly worthy of study.More specifically, we are analyzing, mathematically and experimentally, how value-based algorithms perform in several classes of simulated games of varying complexity from the artificial intelligence community, multi-agent engineering applications drawn from the wireless networking area, and as models of human and animal decision making in collaboration with cognitive神经科学家。在可能的情况下，我们正在完善现有的基于价值的算法，比现有算法更有效，稳健，通常。我们还在设计教育外展活动，包括制作有关如何在现实生活中促进合作行为的娱乐教学视频。