Probabilistic Networks for Automated Reasoning

这是一个基于概率推理和计算实现中的定性概率以前的工作的基础上，基于推理的更新。 一些重要问题有待解决 在 的 采集与处理 的 因果信息这些包括（1）从经验中学习因果结构 数据 使用 组合 的 意见 和操作，以及（2） 不确定性下的规划，使用概率和效用的定性近似。 所采用的方法是间接实验、句法推导和反馈机制。 短期的结果预计将改善推理方法的应用，如 作为 医疗诊断，传感器融合， 制造过程的自动化管理和控制。

This is an accomplishment-based renewal based on previous work in probabilistic reasoning and qualitative probabilities within computational implementations. Some important problems are to be addressed in the acquisition and processing of causal information. These include (1) learning of causal structures from empirical data using combinations of observations and manipulations and (2) planning under uncertainty by using qualitative approximations of probabilities and utilities. The approaches to be pursued are indirect experimentation, syntactic derivations , and feedback mechanisms. The short term results are expected to be improved reasoning methods in applications such as medical diagnosis, sensor fusion, and automated management and control of manufacturing processes.