Manipulating Models in Artificial Intelligence and Operations Research

从供应链的执行到机器人规划，算法决策非常普遍。我的项目将研究AI和OR的模型和解算器的创建、学习、评估、杂交、应用和编排，以推动问题解决能力超出当前技术水平，并发展对这种操纵的基本数学和经验理解。我将在三个主题内研究AI和OR的交叉点。在每一本书中，我将汇集来自计算机科学和数学的不同子领域的理论和算法，以提供新的视角和刺激新的探索途径。主题1：AI与OR中的模型层次结构。复杂的问题常常通过把它们分解成碎片来解决。问题被表示为模型的层次结构，其中求解较高级别的模型定义了较低级别的子空间，而求解较低级别的模型会触发较高级别的细化。这种方法在人工智能中被称为抽象，在或中被称为分解，出现在几种变体中(例如，基于逻辑的Bders分解、SAT模理论)。我将寻求跨域的综合，开发经验驱动的理论，并创建用于动态创建和修改模型层次结构的自动化技术。主题2：序列生成的模型操作。现代机器学习方法在顺序到顺序任务中表现出了显著的性能。ML模型在搜索序列时提供下一个令牌上的概率分布。人工智能规划和组合优化也经常在启发式函数的指导下生成序列。本主题将通过研究人工智能规划和组合优化中的学习模型，学习模型和构建模型的组合，以及从人工智能规划、启发式搜索和组合优化中对ML模型解码的搜索技术的使用和分析，来开发和混合这个交叉点。主题3：协调算法决策系统。现代AI和OR系统需要对应用程序进行建模，解释结果，并在问题和上下文发生变化时协调解算器。这些功能传统上是由训练有素的人类完成的。本主题将研究人工智能、混合主动性用户界面设计和机器人控制体系结构中的认知模型的组合，以开发一个优化代理来自动化这些模型操作。我的发现计划中的工作旨在实现可跨多个应用程序实施的概念级别的进步。这些主题受到现实世界应用的启发和激励，包括高级调度和后勤；机器人任务分配；以及大型数据中心的调度。作为我的探索资助计划的一部分，我还提出了一个基于应用的协作项目，以设计、优化和运营加拿大北部偏远社区的多式联运网络。

Algorithmic decision making is widespread from the execution of supply chains to robot planning. My program will research the creation, learning, evaluation, hybridization, application, and orchestration of the models and solvers of AI and OR to push problem solving power beyond the current state of the art and to develop a fundamental mathematical and empirical understanding of such manipulation. I will work at the intersection of AI and OR within three themes. In each, I will bring together theory and algorithms from disparate subfields of computer science and mathematics to provide new perspectives and spur novel avenues of inquiry. Theme 1: Model Hierarchies in AI and OR. Complex problems are often attacked by breaking them into pieces. A problem is represented as a hierarchy of models where solving a higher level model defines a subspace at a lower level and solving a lower level model triggers refinement at the higher level. This approach, called abstraction in AI and decomposition in OR, appears in several variants (e.g., logic-based Benders decomposition, SAT Modulo Theory). I will seek a synthesis across domains, develop empirically driven theories, and create automated techniques for the dynamic creation and modification of model hierarchies. Theme 2: Model Manipulations for Sequence Generation. Modern machine learning approaches have shown substantial performance in sequence-to-sequence tasks. An ML model provides a probability distribution over the next token as the sequence is searched over. AI planning and combinatorial optimization also often generate sequences, guided by heuristic functions. This theme will develop and hybridize this intersection by investigating learned models in AI planning and combinatorial optimization, the combination of learned and built models, and the use and analysis of search techniques from AI planning, heuristic search, and combinatorial optimization for ML model decoding. Theme 3: Coordinating Algorithmic Decision Making Systems. Modern AI and OR systems require applications to be modeled, results to be interpreted, and the solvers to be coordinated as problems and contexts change. These functions have traditionally been done by highly trained humans. This theme will investigate the combination of cognitive models from AI, mixed initiative user interface design, and robot control architectures to develop an Optimization Agent to automate these model manipulations. The work in my Discovery program is aimed at conceptual level advances that can be implemented across multiple applications. These themes are informed and motivated by real world applications including advanced scheduling and logistics; robot task allocation; and scheduling for large-scale data centres. As part of my Discovery grant program, I am also proposing an application-based collaborative project to design, optimize, and operate the multi-modal transportation network of remote communities in Canada's North.