CAREER: Relational generalization in integrated learning and reasoning

职业：综合学习和推理中的关系泛化

• 批准号: 1942336
• 负责人: Brendan Juba
• 金额: $ 54.35万
• 依托单位: Washington University
• 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 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942336&HistoricalAwards=false
• 关键词: CAREER; Relational; generalization; integrated; learning

In many domains, knowledge may be efficiently expressed in terms of relationships between various entities and quantifiers expressing whether those relationship holds for some or all of those entities. This project will develop algorithms with theoretical guarantees of efficiency and correctness for solving combined learning and reasoning tasks using such expressions. The guarantees will aid our understanding of when the algorithms are safe to use and ensure that they can be used successfully as part of a larger system. Furthermore, students will be trained in research as part of this project. Finally, this project includes the development of a new core undergraduate algorithms course that draws on recent education research to strengthen students' grasp of algorithms and promote participation in computer science among underrepresented groups.Concretely, the expressions used in this project are formulas of first-order logic, and so the project seeks algorithms for reasoning over a collection of first-order formulas that are learned from data consisting of partial truth valuations of the expressions. Formulating such expressions to capture a domain with adequate fidelity for question-answering or planning applications (for example) is a daunting task. Combining the tasks of learning and reasoning has, for other kinds of representations, enabled the design of correct and efficient algorithms for the combined task where the individual tasks were believed to be intractable to solve correctly, as they are here. As part of this work, the researchers will also seek improved algorithms for reasoning with such representations. The project furthermore includes the development of applications of such integrated learning and reasoning in planning problems.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.

在许多领域中，知识可以根据各种实体和量词之间的关系来有效地表达，量词表示这些关系是否适用于这些实体中的一些或全部。 该项目将开发具有效率和正确性理论上保证的算法，用于使用此类表达式解决组合学习和推理任务。这些保证将有助于我们了解算法何时可以安全使用，并确保它们可以成功地用作更大系统的一部分。此外，作为该项目的一部分，学生将接受研究培训。最后，该项目包括开发一门新的核心本科算法课程，该课程借鉴最近的教育研究，以加强学生对算法的掌握，并促进代表性不足的群体参与计算机科学。具体来说，该项目中使用的表达式是一阶逻辑公式，因此该项目寻求对一系列一阶公式进行推理的算法，这些一阶公式是从由部分真值评估组成的数据中学习的。 的表达方式。制定这样的表达式来捕获具有足够保真度的域以用于问答或规划应用程序（例如）是一项艰巨的任务。 对于其他类型的表示，将学习和推理任务结合起来，使得能够为组合任务设计正确且有效的算法，其中单个任务被认为很难正确解决，就像它们在这里一样。作为这项工作的一部分，研究人员还将寻求改进的算法来利用此类表示进行推理。该项目还包括开发此类综合学习和推理在规划问题中的应用。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Learnability with PAC Semantics for Multi-agent Beliefs

多智能体信念的 PAC 语义的可学习性

• DOI: 10.1017/s1471068423000182
• 发表时间: 2023
• 期刊: Theory and Practice of Logic Programming
• 影响因子: 1.4
• 作者: MOCANU, IONELA G.;BELLE, VAISHAK;JUBA, BRENDAN
• 通讯作者: JUBA, BRENDAN

A Scalable Shannon Entropy Estimator

可扩展的香农熵估计器

• DOI: 10.1007/978-3-031-13185-1_18
• 发表时间: 2022
• 期刊: Lecture notes in computer science
• 作者: Golia, P.;Juba, B.;Meel, K.S.
• 通讯作者: Meel, K.S.

Popularizing Fairness: Group Fairness and Individual Welfare

• DOI: 10.1609/aaai.v37i6.25910
• 发表时间: 2023-06
• 作者: Andrew Estornell;Sanmay Das;Brendan Juba;Yevgeniy Vorobeychik

Learning Safe Numeric Action Models

• DOI: 10.1609/aaai.v37i10.26424
• 发表时间: 2023-06
• 作者: Argaman Mordoch;Brendan Juba;Roni Stern