Representations, Inference and Learning for Complex Decision Making Under Uncertainty

这个建议是关于在不确定性下的决策，其中有丰富的关系描述，如医疗病人的电子健康记录中的详细观察条件下作出治疗决定，或作出决定，在一个地理区域的勘探条件下对当地地质的描述。基于统计关系人工智能，概率编程，本体论推理和偏好诱导的最新进展，该提案的目的是设计一个连贯的表达框架，用于不确定性下的推理和决策。概率模型与关系模型的混合被称为统计关系AI。这些模型指定了逻辑结构（根据量化的逻辑变量）和概率结构（根据条件独立性）。利用这两个问题，所谓的提升推理最近已经基本上解决了确切的无向关系的情况下，一些研究小组。该建议的一部分是建立在最近的继任者，以建立下一代的关系概率系统的表达表示，有效的推理和鲁棒的学习。我们还将进一步推进精确推理，这也将使新的方法能够进行有效的近似推理。最近有一个爆炸性的科学和政府数据被发布的词汇定义的正式本体。 我们将致力于表示，推理和学习与异构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层次上定义多个假设，对多个异构数据集进行预测时，会出现令人兴奋的挑战。最终，这些模型被用来做出决策。做决定的另一部分是效用。我们还将研究与关系模型和本体交互的效用模型的偏好启发，并且决策者可以理解。我们的工作将建立在与地质学、计算可持续性和医学领域的现实决策者持续合作的基础上。这个建议是为了发展计算基础，使这种应用可行。*

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.***********