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RI: Small: Anytime Algorithms and Bounds for Probabilistic Graphical Models

RI：小：概率图形模型的随时算法和界限

基本信息

批准号：
2008516
负责人：
Rina Dechter
金额：
$ 45万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008516&HistoricalAwards=false
关键词：
RI Small Anytime Algorithms Bounds

项目摘要

Probabilistic graphical models are employed throughout science and engineering to solve difficult problems, including automated reasoning and decision making, computer vision, computational biology and genetics, and data mining. However, exact inference is often computationally intractable, necessitating approximations or bounds. While significant progress has been made, many real-world problems remain out of reach. Many techniques require a set of problem-specific customizations and choices that must be made in advance, with little guidance or automation. Our research will both improve the performance of probabilistic graphical models and will make these techniques more widely available. The investigators support education and diversity through their undergraduate and graduate teaching, and through making their software available to researchers and to the public.The goal of this research is to develop the next generation of approximate, anytime inference techniques and algorithms for graphical models. Informed by the framework of heuristic search, the investigators will create improved unified schemes for message-passing, vibrational and sampling algorithms. These new algorithms seek to effectively manage models containing mixtures of probabilistic and deterministic relationships, as well as both graph-based and context-specific independence relationships. They will provide meaningful bounds on the results and the accuracy of the algorithms, while simplifying or automating any required tuning to the problem instance, optimizing the inherent trade-offs between complexity and accuracy. The algorithms will be extended to the most challenging tasks (i.e., max-sum-product tasks) such as maximum expected utility queries for optimal decision-making. The investigators will collaborate with domain experts to apply their algorithms to applications such as planning and computational protein design.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.

概率图模型在整个科学和工程中用于解决困难的问题，包括自动推理和决策，计算机视觉，计算生物学和遗传学以及数据挖掘。然而，精确推理通常在计算上是难以处理的，需要近似或界限。虽然取得了重大进展，但现实世界的许多问题仍然无法解决。许多技术需要一组特定于问题的定制和选择，这些定制和选择必须提前进行，几乎没有指导或自动化。我们的研究将提高概率图模型的性能，并使这些技术更广泛地应用。研究人员通过他们的本科和研究生教学支持教育和多样性，并通过使他们的软件提供给研究人员和公众。这项研究的目标是开发下一代近似，随时推断技术和算法的图形模型。通过启发式搜索的框架，研究人员将为消息传递，振动和采样算法创建改进的统一方案。这些新算法寻求有效地管理包含概率和确定性关系的混合模型，以及基于图和特定于上下文的独立关系。它们将为算法的结果和准确性提供有意义的界限，同时简化或自动化对问题实例的任何必要调整，优化复杂性和准确性之间的固有权衡。算法将扩展到最具挑战性的任务（即，最大和积任务），例如用于最优决策的最大期望效用查询。研究人员将与领域专家合作，将他们的算法应用于规划和计算蛋白质设计等应用。该奖项反映了NSF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。