Probabilistic Networks for Automated Reasoning

该项目的长期目标是开发一个全面的概率和因果推理理论，具体到机器解释，以指导计算机系统的建设，能够规划，感知和学习不确定的动态环境。 在这个建议中描述的方法是基于使用概率因果网络作为基本的代表性计划的推理过程。 感兴趣的具体主题是：（1）使用概率、效用、因果关系和反事实关系的定性近似，在不确定性下进行信息融合、诊断和规划;（2）自动生成对行动、建议和意外事件的自然语言解释。这项研究的短期结果将是改进推理方法的应用，如医疗诊断，传感器融合，自动化管理和制造过程的控制。 拟议研究的另一个主要影响将是改进社会和生物科学的实验方法，其中因果推理受到模型uncertainty.http://singapore.cs.ucla.edu/jp_home.html的阻碍。

The long-term objective of this project is to develop a comprehensive theory of probabilistic and causal reasoning, specific enough for machine interpretation, so as to guide the construction of computer systems capable of planning, perceiving, and learning in uncertain dynamic environments. The approaches described in this proposal are based on the use of probabilistic causal networks as the basic representational scheme underlying the reasoning process. Specific topics of interest are (1) Information fusion, diagnosis and planning under uncertainty using qualitative approximations of probabilities, utilities, causal and counterfactual relationships; (2) Automatic generation of natural language explanations of actions, recommendations, and unexpected eventualities. The shorter term results of this research will be improved reasoning methods in applications such as medical diagnosis, sensor fusion, and automated management and control of manufacturing processes. Another major impact of the proposed research will be improved experimental methods in the social and biological sciences, where causal inference is hindered by model uncertainty.http://singapore.cs.ucla.edu/jp_home.html