Probabilistic Networks for Automated Reasoning

这项调查的最终目的是开发能够在动态和不确定环境中自主运行的计算机系统。具体地说，研究包括以下领域的理论和实验研究：(1)利用因果关系和反事实关系在不确定情况下进行信息融合、态势评估、诊断和规划；(2)自动生成行动、建议和意外事件的自然语言解释；以及(3)从数据中学习因果结构，以促进数据密集型应用中的预测和决策。本课程将优先研究产生解释性句子的新方法和分子生物学中的结构学习任务。这项研究的理论部分对我们理解人类知识和科学探究的结构是基本的，并可能在流行病学、社会科学、经济学、医学和生物学等领域产生深远的更广泛的影响，在这些领域，因果知识发挥着主要作用。

The ultimate aim of this investigation is to develop computer systems capable of operating autonomously in dynamic and uncertain environments. Specifically, the investigation comprises theoretical and experimental studies in the following areas: (1) Information fusion, situation assessment, diagnosis, and planning under uncertainty using causal and counterfactual relationships; (2) Automatic generation of natural language explanations of actions, recommendations, and unexpected eventualities; and (3) Learning causal structures from data to facilitate predictions and decisions in data-intensive applications. Priority will be given to new methods of generation explanatory sentences and to structural learning tasks in molecular biology. The theoretical part of the research is fundamental to our understanding the structure of human knowledge and scientific inquiry, and could have far-reaching broader impact in the fields of epidemiology, social science, economics, medicine and biology, where causal knowledge plays a major role.