Automated decision making with rich representations

通过能够做出决定，计算机有望在未来改变世界，甚至比过去所做的还要多。对于智能体（计算机、人类或组织）来说，什么是一个好的决策取决于智能体的信念、偏好和能力。20世纪科学的伟大成果之一是贝叶斯决策分析：理性主体可以根据概率和效用来衡量信念和偏好，从而做出决策。人工智能研究的另一个分支考虑在多个抽象和细节层次上描述对象的丰富表示，使用复杂的本体来描述词汇表。该建议的目的是允许概率推理和决策与多个对象的丰富表示。因此，我们期望智能体具有信念和偏好，能够对具有复杂关系的多个对象进行观察和调节。这包括对对象存在的推理，以及对我们不需要区分的对象集的有效推理。这个项目的挑战是找到认识论上足够充分的表示（足够丰富以解决问题），可以从数据和/或从人那里获得，并且可以有效地推理。我计划利用过去20年来在概率推理方面取得的进展，以及最近的研究成果，例如，如何在没有基础的情况下进行概率推理。这一点很重要，因为很难想象一个应用程序不涉及对多个个体的推理和决策。

Computers promise to change the world in the future, even more than they have done in the past, by being able to make decisions.  What is a good decision for an agent (computer, human, or organization) depends on the agent's beliefs, its preferences and its abilities. One of the great results of 20th century science is Bayesian decision analysis: rational agents can make decisions measuring belief by probabilities and preferences by utilities. Another strand of artificial intelligence research considers rich representations of objects that are described at multiple levels of abstraction and detail, using complex ontologies to describe the vocabulary. The aim of this proposal is to allow probabilistic reasoning and decision making with rich representations of multiple objects. Thus we expect an agent to have beliefs and preferences and be able to observe and condition on multiple objects with complex relationships. This includes reasoning about the existence of objects and reasoning efficiently about sets of objects we don't need to distinguish. The challenges in this project are to find representations that are epistemically adequate (are rich enough to solve the problem), can be learned from data and/or acquired from people, and can be reasoned with efficiently. I plan to leverage the advances in probabilistic reasoning that have been made over the last 20 years, and more recent work showing, for example, how to do probabilistic reasoning without grounding. This is important as it's difficult to imagine an application that does not involve reasoning and decision making about multiple individuals.