Representations, Inference and Learning for Complex Decision Making Under Uncertainty

不确定性下复杂决策的表示、推理和学习

• 批准号: RGPIN-2016-03858
• 负责人: Poole, David
• 金额: $ 2.77万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635087
• 关键词: Representations; Inference; Learning; Complex; Decision

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.

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。