CAREER: Optimization-Based Computational Discovery of Decision-Making Processes

职业：基于优化的决策过程计算发现

• 批准号: 2044077
• 负责人: Qi Zhang
• 金额: $ 52.11万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044077&HistoricalAwards=false
• 关键词: CAREER; Optimization; Based; Computational; Discovery

Decision making is fundamental to everyday life, but many decision-making processes are poorly understood. For example, experts in the operation of chemical plants make decisions based on years of experience, but their decision strategies often are not well documented and, due to the complexity of these manufacturing processes, are difficult to explain even to fellow operators. This means the complete transfer of expert knowledge to new operators remains an unsolved problem. Likewise in microbiology, cells can be considered autonomous agents that make decisions regarding gene expression and cell metabolic function. While we can observe the decisions cells make in experiments, we often do not understand the motivation for these choices. Answering this question would provide fundamental insights that could advance cancer treatment, immunology research, and biomanufacturing operations. These challenges provide the motivation for this research program which aims to develop a computational framework that uses observations of decisions to uncover the underlying decision-making processes. Our research will advance the theory and algorithmic representation of this fundamental problem. Through our integrated research and education activities, we will teach future scientists and engineers to use advanced decision-making tools and promote interdisciplinary collaborations between researchers that work in the field of decision science.Our proposed approach is inspired by the principle of optimality, which conjectures that autonomous agents generally make decisions in some optimal fashion. Following this principle, we propose to model decision-making processes as mathematical optimization problems in which decisions are considered optimal solutions. Given a set of observations, each represented by the decisions made in a specific situation, the goal is to infer the optimization model whose solution results in the observed decisions; this is referred to as Inverse Optimization (IO). The IO approach enjoys all the modeling flexibility provided by mathematical optimization, facilitates incorporation of domain knowledge, and allows the generation of inherently interpretable decision-making models. In this research, we will develop computationally efficient IO algorithms and apply them to a range of problems in science and engineering. Three specific Aims are proposed: (1) learning unknown objective functions, (2) learning unknown constraints, and (3) optimization with IO-based models. Aims 1 and 2 focus on the development of computational methods addressing the challenging aspects of IO, such as nonlinearity, discrete decisions, model selection, and adaptive sampling. Mixed-integer programming, bilevel optimization, and decomposition will be applied in innovative ways to ensure computational tractability. In Aim 3, we will demonstrate how optimization models derived from IO can not only help discover hidden decision-making processes but also serve as surrogate optimizers and embedded models in hierarchical optimization, with specific applications in bioprocess optimization and environmental policy design. Because the principle of optimality enjoys broad (albeit often approximate) validity and the IO methods developed in our research will be generalizable, our work has the potential to broadly impact artificial intelligence research, robotics, biology, healthcare, and even management and behavioral science. We will pursue a set of activities that include teaching K-12 students the basic concepts of decision science through games, incorporating optimization into our chemical engineering curriculum, establishing a short course on decision making, and organizing cross-disciplinary workshops.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.

决策对于日常生活至关重要，但是许多决策过程对此很了解。例如，化工厂运营的专家根据多年的经验做出决策，但是他们的决策策略通常没有得到充分的记录，并且由于这些制造过程的复杂性，甚至对其他运营商也很难解释。这意味着将专家知识完全转移给新运营商仍然是一个未解决的问题。同样，在微生物学中，可以将细胞视为自主剂，可以决定基因表达和细胞代谢功能。尽管我们可以观察到实验中细胞做出的决策，但我们通常不了解这些选择的动机。回答这个问题将提供基本的见解，以推动癌症治疗，免疫学研究和生物制造操作。这些挑战为该研究计划提供了动力，该计划旨在开发一个计算框架，该计算框架使用决策观察来揭示基本决策过程。我们的研究将推进这个基本问题的理论和算法表示。通过我们的综合研究和教育活动，我们将教导未来的科学家和工程师使用先进的决策工具，并促进在决策科学领域工作的研究人员之间的跨学科合作。我们所提出的方法受到最佳原则的启发，这些原则是猜想的。该自主代理通常以某种最佳方式做出决策。遵循这一原则，我们建议将决策过程建模为数学优化问题，其中决策被视为最佳解决方案。给定一组观察结果，每种观察都以特定情况下的决策表示，目标是推断其解决方案会导致观察到的决策的优化模型；这称为反优化（IO）。 IO方法享受数学优化提供的所有建模灵活性，促进域知识的融合，并允许生成固有的可解释的决策模型。在这项研究中，我们将开发计算高效的IO算法，并将其应用于科学和工程领域的一系列问题。提出了三个具体目标：（1）学习未知目标功能，（2）学习未知的约束以及（3）基于IO的模型的优化。目标1和2的重点是开发计算方法，以解决IO的具有挑战性方面，例如非线性，离散决策，模型选择和自适应抽样。混合企业编程，双光线优化和分解将以创新的方式应用，以确保计算障碍。在AIM 3中，我们将演示从IO中得出的优化模型不仅可以帮助发现隐藏的决策过程，而且还可以作为层次优化的替代优化器和嵌入式模型，以及在生物过程优化和环境策略设计中的特定应用。由于最佳原则具有广泛的（尽管经常近似）的有效性，并且我们的研究中开发的IO方法将是可推广的，因此我们的工作有可能广泛影响人工智能研究，机器人技术，生物学，医疗保健甚至管理和行为科学。我们将开展一系列活动，包括通过游戏教授K-12学生的决策科学基本概念，将优化纳入我们的化学工程课程，建立有关决策的简短课程，并组织跨学科研讨会。这一奖项反映了NSF的奖项。法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。

项目成果

Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization

• DOI: 10.1287/ijoc.2022.1162
• 发表时间: 2020-09
• 期刊: ArXiv
• 作者: Rishabh Gupta-;Qi Zhang

Kinetic‐model‐based pathway optimization with application to reverse glycolysis in mammalian cells

基于动力学模型的途径优化及其在哺乳动物细胞中逆转糖酵解的应用

• DOI: 10.1002/bit.28249
• 发表时间: 2023
• 期刊: Biotechnology and Bioengineering
• 影响因子: 3.8
• 作者: Lu, Yen‐An;Brien, Conor M.;Mashek, Douglas G.;Hu, Wei‐Shou;Zhang, Qi
• 通讯作者: Zhang, Qi

Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization

• DOI: 10.1016/j.compchemeng.2022.108123
• 发表时间: 2022-10
• 期刊: Comput. Chem. Eng.
• 作者: Rishabh Gupta;Qi Zhang

Decision-Focused Surrogate Modeling with Feasibility Guarantee

具有可行性保证的以决策为中心的代理建模

• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Gupta, Rishabh;Zhang, Qi
• 通讯作者: Zhang, Qi