Probabilistic Networks for Automated Reasoning

这个项目的长期目标是发展一个全面的概率和因果推理理论，足够具体到机器解释，从而指导计算机系统的建设，能够在不确定的动态环境中规划、感知和学习。本提案中描述的方法是基于使用概率因果网络作为推理过程的基本表征方案。感兴趣的具体主题是(1)不确定情况下的信息融合、诊断和规划，使用概率、效用、因果关系和反事实关系的定性近似；(2)行动、建议和意外事件的自然语言解释的自动生成。这项研究的短期结果将是在医疗诊断、传感器融合和制造过程的自动化管理和控制等应用中改进推理方法。拟议研究的另一个主要影响将是改进社会科学和生物科学的实验方法，其中因果推理受到uncertainty.http://singapore.cs.ucla.edu/jp_home.html模型的阻碍

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