Graphical models: Inference, decision and acquisition

我的研究涉及智能系统，即所谓的代理，它在不确定或基于约束的环境中发挥作用，无论是单独的还是协同的。它专注于知识表示的图形模型，并涵盖了知识获取、推理和决策制定的问题。我之前的研究已经建立了几类多智能体图形模型，称为MSBN用于概率推理，DMSBN用于预测，CDN用于设计决策。MSBN和DMSBN中的代理通信由一个代理(根)发起，其选举产生开销。为了提高灵活性，将调查一个没有根基的政权的可行性。还将研究多主体规划，重点放在在线规划上，而不是通常追求的线下决策，以提高效率。 我之前对多智能体约束图形模型(MSCN)的研究证实，从多智能体概率图形模型中吸取的经验教训可以有效地扩展到分布式约束满足(DCSP)和优化(DCOP)。该研究将探索MSCN的底层运行时表示中嵌入的结构，以实现更高效的约束推理。基于CDN和MSCN的某些独特的、理想的计算特性(相对于现有的DCOP框架)，通过推广CDN和MSCN，建立了DCOP的多智能体图形模型。 以前的研究开发了因果模型，NIN-和树，用于在构建贝叶斯网络时有效地获取条件概率表(CPT)。这些模型将现有模型的表达能力从加强交互扩展到破坏和融合两者。建议的研究将研究将任意CPT近似为NIN-AND树，通过数据挖掘获得这些模型的算法，以及直接将NIN-AND树结合到推理中以提高效率。

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.