RI:Small:Collaborative Research:Scalable Decentralized Planning for Open Multiagent Environments

RI：小型：协作研究：开放多代理环境的可扩展去中心化规划

• 批准号: 1910037
• 负责人: Prashant Doshi
• 金额: $ 14.55万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910037&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Scalable

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.

自动化规划是指寻找一系列预期能够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，计划就会变得困难。如果存在其他代理，其行为也会影响环境和奖励结果，情况会变得更加复杂。虽然这两个挑战都受到了研究人员的广泛关注，但现实世界的环境往往表现出另一个特性——代理和任务的开放性。当智能体退出环境、恢复或新智能体进入时，就会出现智能体开放性；当智能体必须完成的任务发生变化、新任务出现或一些任务消失时，就会出现任务开放性。 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的任务从环境中消失。 该研究正在系统地推广自动化规划，以考虑这些新的实际挑战，同时仍然保持方法在计算上的可行性。这项研究涉及欧柏林学院（主要是本科院校）、内布拉斯加州大学和佐治亚大学的研究人员密切合作，开发开放多智能体系统中的规划方法，并在野火扑灭、动态拼车和其他表现出开放性的领域进行演示。主要研究人员正在利用这项研究的结果来指导他们的课堂教学，并计划在欧柏林举办针对中小学生的人工智能训练营。技术方法包括对代理和任务开放性对环境的影响有一个基本的了解，并利用这种理解来开发和学习代表开放性的随机模型。这些模型被用来构建新的算法，以便在这种情况下进行易于处理的代理级规划。这些方法将利用系统级属性，例如代理匿名性和统计群体抽样，允许从小样本对大群体进行建模，这在社会科学领域已取得成功，使这些方法可扩展到许多代理。这项研究正在加深我们对智能代理如何在复杂环境中执行可扩展、去中心化规划的理解，并开发一个具有实证结果和见解的框架，这可能会为个人助理代理带来更强大的智能，以进行人与代理的交互。机器人和自动驾驶汽车，当参与者及其任务随着时间的推移而变化时，代理会推理出具有挑战性的环境动态。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Decision-theoretic planning with communication in open multiagent systems

开放多智能体系统中通信的决策理论规划

• DOI: 10.48550/arxiv.2310.08089
• 发表时间: 2024-09-13
• 期刊: ArXiv
• 作者: Anirudh Kakarlapudi;Gayathri Anil;A. Eck;Prashant Doshi;Leen
• 通讯作者: Leen

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

开放式和类型化多智能体系统中的可扩展决策理论规划

• DOI: 10.1609/aaai.v34i05.6200
• 发表时间: 2020-06
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Eck, Adam;Shah, Maulik;Doshi, Prashant;Soh, Leen
• 通讯作者: Soh, Leen