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

自动化计划是关于找到一系列预期能成功完成手头任务或最大化所获回报的行动。当行动的结果不确定时，计划就变得困难。在其他代理人的存在下，情况更加复杂，这些代理人的行为也会影响环境和奖励结果。虽然这两个挑战都受到了研究人员的极大关注，但现实世界的情景往往表现出另一种属性--代理和任务开放性。当代理退出环境、继续或进入新代理时，代理开放；当代理必须完成的任务发生变化，出现新任务和一些任务消失时，任务开放。这种开放性使计划过程变得复杂，因为代理现在需要最佳地考虑现有队友离开环境的可能性，或者从环境中消失一个成功的有回报的任务。这项研究正在系统地推广自动化规划，以考虑这些新的和实际的挑战，同时仍然保持这些方法在计算上的可行性。这项研究涉及奥伯林学院(主要是本科院校)、内布拉斯加州大学和佐治亚州大学的研究人员密切合作，开发在开放的多智能体系统中进行规划的方法，并在野火扑灭、动态拼车和其他表现出开放性的领域中展示这些方法。主要研究人员正在利用这项研究的结果为他们的课堂教学提供信息，并在奥伯林计划为中小学生举办人工智能夏令营。技术方法包括从根本上了解代理和任务开放性对环境的影响，并利用这种理解来开发和学习代表开放性的随机模型。这些模型正被用来建立新的算法，以便在这种情况下进行易处理的代理级规划。这些方法将利用系统级的属性，如代理匿名性和统计人口抽样，允许从小样本建模大群体，这在社会科学中已取得成功，使方法可扩展到许多代理。这项研究促进了我们对智能代理如何在复杂环境中执行可扩展的、分散的规划的理解，并开发了一个框架--具有经验结果和见解--可以为个人助理代理带来更强大的智能，用于人-代理交互、机器人和自动驾驶车辆，其中代理推理挑战环境动态，因为参与者及其任务随着时间的变化而变化。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.