Large scale reasoning under uncertainty

个人和社会越来越多地面临着基于大量信息做出决策的问题，他们需要有一种原则性的方法来判断这些信息。受到地质学应用的启发，这一提议的第一个推动力是建立我们所谓的“语义科学”的基础（类似于语义网，但用于科学数据和假设），允许实现语义互操作性的本体，提供证据的观察数据，以及对数据进行（概率）预测的假设，并可用于形成模型以针对特定情况进行预测。使所有这些工作在一起是一个重大的研究挑战。 不确定性下的空间规划，如在林业应用中出现的，其中行动和效用的影响不是局部的，是第二个推力。我们正在研究策略梯度方法，迭代地改善关系表示的政策。 第三个推力是概率关系模型的有效推理。 在2003年，我首次提出了解除推理，即我们不区分我们拥有相同信息的个体。已经有了相当大的进步，但我们仍然没有达到“圣杯”，即随着不可区分的个体数量的增加，我们可以以指数级的速度进行推理，而不是接地，尽管似乎有这样一种方法。这有望成为下一代概率建模和编程语言的基础。 第四个领域是关于存在和身份不确定性的推理。典型的模型指的是个体的角色，但对世界的观察并没有指定哪些个体（如果有的话）履行模型的角色。这项工作需要在表示以及推理和学习算法方面取得进展。

Individuals and society are increasingly faced with making decisions based on enormous amounts of information and they need to have a principled way to judge that information. Inspired by applications in geology, the first thrust of this proposal is to build the foundations of what we have called "semantic science" (in analogy with the semantic web, but for scientific data and hypotheses), to allow for ontologies that enable semantic interoperability, observational data that provides evidence, and hypotheses that make (probabilistic) predictions on data and can be used to form models to make predictions for specific cases. Making these all work together is a major research challenge. Spatial planning under uncertainty, such as arise in forestry applications, where effects of actions and utilities are not local is the second thrust. We are investigating policy gradient methods that iteratively improve relational representations of policies. A third thrust is in efficient inference for probabilistic relational models. In 2003 I first proposed lifted inference, where we do not distinguish individuals about which we have the same information. There has been considerable advances, but we still have not reached the "holy grail" where we can do inference exponentially faster than grounding as the number of indistinguishable individuals increases, although it seems plausible that there is such a method. This promises to form a foundation of the next generation of probabilistic modelling and programming languages. A fourth area is in reasoning about existence and identity uncertainty. Typically models refer to roles of individuals, but the observations of the world do not specify which individuals, if any, fulfill the roles of the models. This work requires advances in representations as well as reasoning and learning algorithms.