权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

RI：小：数据驱动的易于处理的决策理论规划

基本信息

批准号：
1815598
负责人：
Prashant Doshi
金额：
$ 46.65万
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：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815598&HistoricalAwards=false
关键词：
RI Small Tractable Decision Theoretic

项目摘要

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.

自动规划是关于找到一系列预期成功完成手头任务的行动。决策理论规划将自动规划作为一系列决策，每一个决策都优化了规划者的短期和长期偏好。这种自动规划的方法允许现实的行动，其结果往往是不确定的，除了精确的目标外，还可以根据规划者可能不精确的偏好进行推理。然而，决策理论规划依赖于规划问题的准确规范，这通常是不切实际的，并且计算成本非常高。本研究通过调查一种新的和有意义的规划问题表示来解决这些挑战，该问题表示直接从数据中学习，这表明需要繁琐的规范。该表示被设计为产生更有效的解决方案的计算。因此，这项研究有可能将自动规划过渡到大型实用应用中，例如在高密度计算机网络中的流量路由，这将在本项目中得到证明。该项目将培训研究生进入人工智能的一个重要领域的劳动力，并将促进美国和加拿大研究人员之间的国际研究合作。PI将使用这项研究的结果来通知他的课堂教学，这将为学生提供如何接触自动规划可以对社会有用。技术方法是将以前的两个进展合并起来，开发一个新的图形模型，称为动态和积最大网络。在这些之前的线程中，PI广义和积网络允许在两个方向上进行有效的概率推理。首先，沿着时间维度，从而允许对一系列变量的推断，其次，通过包括决策和效用变量来实现有效的非顺序决策。本研究是调和的基本困难的决策理论规划与动态和-积-最大网络的效率，通过研究哪一类规划问题可以compensitively表示的新模型。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适当的模式，并创建了数据集的评估测试平台。最后一个推力是开发一系列方法，用于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。该研究计划预计将产生一个新的图形表示和相关的方法，允许有效的数据驱动的规划，其效用将通过与工业界合作的实际应用证明。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Anytime Learning of Sum-Product and Sum-Product-Max Networks

随时学习 Sum-Product 和 Sum-Product-Max 网络

DOI：
发表时间：
2022
期刊：
The 11th International Conference on Probabilistic Graphical Models
影响因子：
0
作者：
Pawar, Swaraj;Doshi, Prashant
通讯作者：
Doshi, Prashant

Data-Driven Decision-Theoretic Planning using Recurrent Sum-Product-Max Networks

使用循环和积最大网络的数据驱动决策理论规划

DOI：
发表时间：
2021
期刊：
Proceedings of the International Conference on Automated Planning and Scheduling
影响因子：
0
作者：
Tatavarti, Hari;Doshi, Prashant;Hayes, Layton
通讯作者：
Hayes, Layton

State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability

部分可观测性下决策理论规划的基于状态的循环 SPMN

DOI：
10.24963/ijcai.2021/348
发表时间：
2021
期刊：
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track.
影响因子：
0
作者：
Hayes, Layton;Doshi, Prashant;Pawar, Swaraj;Tatavarti, Hari Teja
通讯作者：
Tatavarti, Hari Teja

Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks

前馈和循环和积网络的在线结构学习

DOI：
发表时间：
2018
期刊：
Advances in Neural Information Processing Systems (NeurIPS
影响因子：
0
作者：
Kalra, Agastya;Rashwan, Abdullah;Hsu, Wei-Shou;Poupart, Pascal;Doshi, Prashant;Trimponias, Georgios
通讯作者：
Trimponias, Georgios