Graphical models: Inference, decision and acquisition

我的研究涉及智能系统，被称为代理，在不确定或基于约束的环境中，无论是单独或合作。它侧重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智能体图形模型，称为概率推理的MSBN，预测的DMSBNs和设计决策的CDN。MSBN和DMSBN中的代理通信由一个代理（根）发起，并且其选举会产生开销。为了提高灵活性，将研究无根源制度的可行性。 还将研究多智能体规划，重点是在线规划，而不是通常追求的离线决策，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.