Graphical models: Inference, decision and acquisition

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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.