RI: Small: Collaborative Research: RUI: Scalable Decentralized Planning in Open Multiagent Environments

RI：小型：协作研究：RUI：开放多代理环境中的可扩展去中心化规划

• 批准号: 1909513
• 负责人: Adam Eck
• 金额: $ 20.59万
• 依托单位: Oberlin College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909513&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; RUI

Automated planning is about finding a sequence of actions that is anticipated to successfully complete the task at hand or maximize earned rewards. Planning becomes difficult when the outcomes of actions are uncertain. It is further complicated in the presence of other agents whose actions also affect the environment and reward outcomes. While both these challenges have received much attention from researchers, real-world contexts often exhibit another property -- that of agent and task openness. Agent openness comes about when agents exit the environment, resume, or new agents enter, and task openness occurs when the tasks that agents must complete change with new tasks appearing and some disappearing. Such openness complicates the planning process as agents now need to optimally consider, for example, the possibilities of existing teammates leaving the environment or a successfully rewarding task disappearing from the environment. The research is systematically generalizing automated planning to consider these new and practical challenges while still keeping the methods computationally feasible. This research involves investigators at Oberlin College (a primarily undergraduate institution), Universities of Nebraska and Georgia collaborating closely to develop methods for planning in open multi-agent systems and demonstrating them in domains such as wildfire suppression, dynamic ridesharing, and others that exhibit openness. The principal investigators are using the outcomes of this research to inform their classroom instructions, and artificial intelligence camps for elementary and middle school students are planned at Oberlin.The technical approach involves gaining a fundamental understanding of the impact of agent and task openness on the environment, and utilizing this understanding to develop and learn stochastic models that represent the openness. These models are being used to build new algorithms for tractable agent-level planning in such contexts. The methods will exploit system-level properties such as agent anonymity and statistical population sampling that allows modeling large populations from small samples, which has been successful in the social sciences to make the approaches scalable to many agents. This research is advancing our understanding of how intelligent agents should perform scalable, decentralized planning in complex environments, and developing a framework--with empirical results and insights--that could lead to more robust intelligence for personal assistant agents for human-agent interactions, robots, and autonomous vehicles, where the agents reason about challenging environmental dynamics as the actors and their tasks change over time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自动化计划是关于找到一系列动作，这些动作预计可以成功完成手头任务或最大化奖励的奖励。当动作的结果不确定时，计划就变得困难。在其他代理人也影响环境和奖励结果的其他代理商面前，它更加复杂。尽管这两个挑战都受到了研究人员的极大关注，但现实世界中的环境经常表现出另一个特性 - 代理和任务开放性。当代理退出环境，简历或新代理输入时，就会出现代理商的开放性，并且当代理必须通过出现新任务并消失的新任务完成更改的任务时，就会发生任务开放性。 这种开放性使计划过程变得复杂，因为代理商现在需要最佳考虑，例如，现有队友离开环境的可能性或成功从环境中消失的成功奖励任务。 这项研究正在系统地概括自动化计划，以考虑这些新的和实际的挑战，同时仍保持这些方法在计算上可行。这项研究涉及Oberlin College（主要是本科机构）的研究人员，内布拉斯加州和佐治亚大学仔细合作，以开发在开放的多代理系统中计划计划的方法，并在诸如野火抑制，动态乘车场以及其他表现开放的领域中证明它们。首席研究人员正在使用这项研究的结果来告知他们的课堂说明，并且在Oberlin计划了针对小学生和中学生的人工智能营地。技术方法涉及对代理和任务对环境的影响进行基本了解，并利用这种理解和学习代表开放性的随机模型。这些模型用于在这种情况下构建针对可拖动代理级规划的新算法。这些方法将利用系统级属性（例如代理匿名性和统计种群抽样），这些属性允许对小样本进行建模，这些样本在社会科学中已经成功，以使这些方法可扩展到许多代理商。这项研究正在促进我们对智能代理如何在复杂环境中执行可扩展，分散的计划，并开发一个框架（以及经验结果和见解）的框架 - 可能会导致个人助理互动，机器人，机器人和自主工具的个人助理互动的智力，并在其中推理了代理人的统计范围和统计环境的统计范围，并宣告了统计的统计范围，并宣告了统计范围的统计范围。认为值得通过基金会的智力优点和更广泛影响的评论标准来评估值得支持。

项目成果

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

• DOI: 10.1609/aaai.v34i05.6200
• 发表时间: 2019-11
• 作者: A. Eck;Maulik Shah;Prashant Doshi;Leen-Kiat Soh

Decision-theoretic planning with communication in open multiagent systems

• 发表时间: 2022
• 作者: Anirudh Kakarlapudi;Gayathri Anil;A. Eck;Prashant Doshi;Leen-Kiat Soh