Representations, Inference and Learning for Complex Decision Making Under Uncertainty

这项建议是关于在具有丰富关系描述的领域的不确定性下的决策，例如根据患者电子健康记录中的详细观察来做出关于患者的治疗决策，或者根据对当地地质的描述来做出关于在地理区域进行勘探的决策。基于统计关系人工智能、概率规划、本体论推理和偏好诱导的最新进展，该建议的目的是为不确定情况下的推理和决策设计一个连贯的表达框架。*概率模型和关系模型的混合被称为统计关系人工智能。这些模型规定了逻辑结构(根据量化的逻辑变量)和概率结构(根据条件独立性)。利用两者的问题被称为提升推理，最近许多研究团队基本上解决了确切的非定向关系情况。该建议的一部分是建立在最近的继任者的基础上，以建立具有表达能力、高效推理和稳健学习的下一代关系概率系统。我们还将进一步推进精确推理，这也将使进行有效的近似推理的新方法成为可能。*最近，用形式本体定义的词汇表发布了大量的科学和政府数据。我们将致力于表示、推理和学习与异类数据集和丰富的本体互操作的假设。当在不同的抽象和细节级别定义多个假设以对多个异类数据集进行预测时，会出现令人兴奋的挑战。*最终，这些模型被用来做出决策。做出决定的另一个部分是公用事业。我们还将致力于效用模型的偏好引出，这些模型与关系模型和本体交互，并为决策者所理解。*我们的工作将建立在与真实世界的决策者在地质学、计算可持续性和医学方面的持续合作的基础上。这项提议是为了发展计算基础，使这类应用变得可行。

This proposal is about decision making under uncertainty in domains where there are rich relational descriptions, such as making treatment decisions about medical patients conditioned on the detailed observations in their electronic health records, or making a decision about prospecting in a geographic region conditioned on a description of the local geology. Building on recent progress in statistical relational AI, probabilistic programming, ontological reasoning and preference elicitation, the aim of this proposal is to design a coherent expressive framework for reasoning and decision making under uncertainty.******The mix of probabilistic models with relational models has become known as statistical relational AI. These models specify both the logical structure (in terms of quantified logical variables) and the probabilistic structure (in terms of conditional independence). The problem of exploiting both, called lifted inference has recently been essentially solved for the exact undirected relational case by a number of research teams. Part of this proposal is to build on the recent successors to build the next generation of relational probabilistic systems with expressive representations, efficient inference and robust learning. We will also further advance exact inference, which will also enable new ways to do efficient approximate inference.******Recently there has been an explosion of scientific and government data being published with the vocabulary defined by formal ontologies. We will work on representing, reasoning and learning hypotheses that interoperate with heterogeneous data sets and rich ontologies. There are exciting challenges that arise when defining multiple hypotheses at various levels of abstraction and detail that make predictions on multiple heterogeneous data sets.******Ultimately these models are used to make decisions. The other part of making decisions is utilities. We will also work on preference elicitation for utility models that interact with the relational models and ontologies, and are understandable by decision makers.******Our work will build on ongoing collaboration with real-world decision makers in geology, computational sustainability, and medicine. This proposal is to develop the computational foundations to make such applications feasible.***********