Graphical models: Inference, decision and acquisition

图模型：推理、决策和获取

• 批准号: 155425-2011
• 负责人: Xiang, Yang
• 金额: $ 1.02万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=507875
• 关键词: Graphical; models; Inference; decision; acquisition

My research concerns intelligent systems, known as agents, that function in uncertain or constraint-based environments, either individually or cooperatively. It focuses on graphical models for knowledge representation and covers issues on knowledge acquisition, inference, and decision making. My previous research has established several classes of multiagent graphical models, known as MSBNs for probabilistic reasoning, DMSBNs for forecasting, and CDNs for design decision making. Agent communication in MSBNs and DMSBNs is initiated by one agent (the root), and its election incurs overhead. To improve flexibility, feasibility of an unrooted regime will be investigated. Multiagent planning will also be studied focusing on online planning, rather than commonly pursued offline policy making, to gain efficiency.

我的研究涉及智能系统（称为代理），该系统单独或合作地在不确定或基于约束的环境中起作用。它着重于用于知识表示的图形模型，并涵盖了知识获取，推理和决策的问题。 我以前的研究已经建立了几类多基因图形模型，称为概率推理的MSBN，用于预测的DMSBN和用于设计决策的CDN。 MSBN和DMSBN中的代理通信是由一个代理（根）启动的，其选举会引​​起开销。为了提高灵活性，将研究一个无根状态的可行性。 还将研究多种计划，专注于在线计划，而不是通常追求离线政策制定，以提高效率。