RI:Small:Tractable Decision-Theoretic Planning Driven by Data

自动化计划是关于找到一系列预期能成功完成手头任务的行动。决策理论规划将自动化规划作为一系列决策，每个决策都优化了规划者的近期和长期偏好。这种自动化计划的方法允许现实的行动，这些行动的结果往往是不确定的，并且除了精确的目标之外，计划者可能还会有不精确的偏好。然而，决策理论规划依赖于规划问题的精确规范，这通常是不切实际的，并且计算成本非常高。本研究通过研究一种新的、有意义的规划问题表示来解决这些挑战，这种表示直接从数据中学习，从而减轻了对冗长规范的需求。这种表示是为了更有效地计算解而设计的。因此，这项研究有可能将自动化规划转变为大型实用应用，例如高密度计算机网络中的流路由，这将在本项目中得到证明。该项目将培养研究生进入人工智能重要领域的劳动力，并将促进美国和加拿大研究人员之间的国际研究合作。PI将利用这项研究的结果来指导他的课堂教学，这将使学生了解自动化规划如何对社会有用。技术上的方法是将之前的两种进展结合起来，开发一种新的图形模型，称为动态和积最大网络。在前面的这些线程中，PI在两个方向上推广了和积网络，它允许有效的概率推理。首先，沿着时间维度，从而允许对一系列变量进行推理；其次，通过包含决策变量和效用变量，实现有效的非顺序决策制定。本研究通过研究哪一类规划问题可以用新模型紧凑地表示，调和了决策理论规划的基本困难与动态和积最大网络的效率。由于这些模型可以直接从数据中学习，因此研究还为数据建立了合适的模式，并创建了数据集的评估测试平台。最后一个重点是开发一套方法，从适当的数据中自动学习动态和积最大网络的结构和参数，重点是学习有效的模型。该研究计划有望产生一种新的图形表示和相关方法，从而实现有效的数据驱动规划，其效用将在与工业合作的实际应用中得到证明。该奖项反映了美国国家科学基金会的法定使命，并通过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，被认为值得支持。

Automated planning is about finding a sequence of actions that is expected to successfully complete the task at hand. Decision-theoretic planning approaches automated planning as a sequence of decisions, each of which optimizes the planner's combined immediate and longer-term preferences. This approach to automated planning allows for realistic actions whose outcomes are often uncertain and reasons with the planner's possibly inexact preferences in addition to precise goals. However, decision-theoretic planning relies on an accurate specification of the planning problem, which is often impractical and is computationally very costly. This research is addressing these challenges by investigating a new and meaningful planning problem representation that is learned directly from data, which alleviates the need for tedious specifications. The representation is designed to yield more efficient computation of solutions. Consequently, this research has the potential to transition automated planning to large pragmatic applications, such as in flow routing in high-density computer networks, which will be demonstrated in this project. The project will train graduate students for entering the workforce in an important area of artificial intelligence, and it will facilitate an international research collaboration between researchers in US and Canada. The PI will use the outcomes of this research to inform his classroom instruction, which will provide students with exposure to how automated planning can be useful to the society. The technical approach is merging two threads of previous progress toward developing a new graphical model called the dynamic sum-product-max network. In these previous threads, the PI generalized sum-product networks, which allow efficient probabilistic inference, in two directions. First, along the temporal dimension thereby allowing inference over a sequence of variables, and second, enabling efficient non-sequential decision making by including decision and utility variables. This research is reconciling the fundamental hardness of decision-theoretic planning with the efficiency of dynamic sum-product-max networks by studying which class of planning problems can be compactly represented by the new model. As these models can be directly learned from data, the research is also establishing the appropriate schema for the data and creating an evaluation testbed of datasets. A final thrust is developing a portfolio of methods for automatically learning both the structure and parameters of dynamic sum-product-max networks from appropriate data, with a focus on learning valid models. The research plan is expected to yield a new graphical representation and associated methods that allow efficient data-driven planning whose utility will be demonstrated by real-world applications in collaboration with industry.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.