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.

自动规划是关于找到一系列预期成功完成手头任务或最大化获得奖励的行动。当行动的结果不确定时，规划就变得困难。当其他代理人的行为也会影响环境和奖励结果时，情况会进一步复杂化。虽然这两个挑战都受到了研究人员的广泛关注，但现实世界的背景往往表现出另一个属性-代理和任务开放性。当代理退出环境，恢复或新代理进入时，代理开放性就出现了，而当代理必须完成的任务随着新任务的出现和消失而发生变化时，任务开放性就出现了。 这种开放性使规划过程变得复杂，因为智能体现在需要最佳地考虑，例如，现有队友离开环境的可能性或成功奖励任务从环境中消失的可能性。 该研究正在系统地推广自动规划，以考虑这些新的和实际的挑战，同时仍然保持计算方法的可行性。这项研究涉及奥伯林学院（主要是本科院校），内布拉斯加大学和格鲁吉亚大学的研究人员密切合作，开发开放式多智能体系统的规划方法，并在野火抑制，动态乘车等领域展示它们。主要研究人员正在使用这项研究的结果来通知他们的课堂教学，并计划在奥伯林为小学和中学生举办人工智能夏令营。技术方法包括获得对代理和任务开放性对环境影响的基本理解，并利用这种理解来开发和学习代表开放性的随机模型。这些模型被用来建立新的算法，在这种情况下，听话的代理级规划。这些方法将利用系统级属性，如代理匿名性和统计人口抽样，允许从小样本建模大人口，这在社会科学中已经取得成功，使方法可扩展到许多代理。这项研究正在推进我们对智能代理应该如何在复杂环境中执行可扩展的分散规划的理解，并开发一个框架-具有经验结果和见解-可以为人类代理交互，机器人和自动驾驶汽车的个人助理代理带来更强大的智能，该奖项反映了NSF的法定使命，并被认为是值得支持的，使用基金会的知识价值和更广泛的影响审查标准进行评估。

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