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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569463
• 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. My previous research on multiagent constraint graphical models, known as MSCNs, confirms that lessons learned from multiagent probabilistic graphical models can be usefully extended into distributed constraint satisfaction (DCSP) and optimization (DCOP). The proposed research will explore the structure embedded in lower level runtime representation of MSCNs for more efficient constraint reasoning. Motivated by certain unique, desirable computational properties of CDNs and MSCNs (relative to existing frameworks for DCOP), a multiagent graphical model for DCOP will be developed by generalizing CDNs and MSCNs. Previous research developed causal models, NIN-AND trees, for efficient acquisition of conditional probability tables (CPTs) in constructing Bayesian networks. These models extend the expressive power of existing models from reinforcing interaction to undermining and mixture of the two. The proposed research will investigate approximating an arbitrary CPT as an NIN-AND tree, algorithms to acquire these models by data mining, and direct incorporation of NIN-AND trees into inference to improve 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。 我先前对多构约束图形模型（称为MSCN）的研究证实，从多基因概率图形模型中学到的经验教训可以将其扩展到分布式约束满意度（DCSP）和优化（DCOP）中。 拟议的研究将探索MSCN较低级运行时表示中的结构，以进行更有效的约束推理。 由CDN和MSCN的某些独特，理想的计算属性（相对于DCOP的现有框架）的动机，将通过概括CDN和MSCN来开发DCOP的多构图形模型。 先前的研究开发了因果模型，NIN和树木，用于在构建贝叶斯网络中有效地获取条件概率表（CPTS）。 这些模型将现有模型的表达能力从加强相互作用到破坏和混合物的混合。 拟议的研究将调查将任意CPT作为NIN和树，算法通过数据挖掘获得这些模型的算法，并将NIN和树直接融合到推理中以提高效率。