Large scale reasoning under uncertainty

个人和社会越来越多地面临着根据海量信息做出决策的问题，他们需要有一种有原则的方式来判断这些信息。受地质学应用的启发，这一提议的第一要旨是建立我们所称的“语义科学”的基础(类似于语义网，但对于科学数据和假设)，以允许实现语义互操作性的本体、提供证据的观测数据以及对数据进行(概率)预测并可用于形成模型以对特定情况进行预测的假设。让所有这些都发挥作用是一项重大的研究挑战。 不确定性下的空间规划是第二个推动力，例如在林业应用中，行动和公用事业的影响不是局部的。我们正在研究迭代地改进策略的关系表示的策略梯度方法。 第三个推动力是对概率关系模型的有效推断。2003年，我首次提出解除推理，即我们不区分拥有相同信息的个人。虽然已经有了相当大的进步，但我们仍然没有达到“圣杯”，在这种情况下，随着无法区分的个体数量的增加，我们可以指数级地比接地更快地进行推理，尽管似乎有这样一种方法是可信的。这有望形成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。通常，模型指的是个人的角色，但对世界的观察并不指定哪些人(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.