Pragmatic Approaches to Reasoning Under Uncertainty (Computer and Information Science)

本研究探讨了务实的方法， 计算机概率推理系统 在过去， 人工智能研究人员经常回避概率 由于共同的感知而不确定的推理理论 概率论的应用总是与 致力于复杂的算法和无节制的 知识获取时间 发展高效 算法和评估技术将使研究人员能够 应用理论上合理的信念蕴涵模型， 复杂的问题 因此，本研究的目的是（1） 发展运用知识解决问题的技巧， 动态优化计算机性能价值的权衡 （2）构造有效的概率算法， 推理，（3）研究语用技巧， 从专家那里获取知识。2现有诊断 系统是这项研究的试验平台，它直接适用于 科学和工程的许多领域。 尽管人工智能技术正在被 用于提供决策支持工具（“专家系统”）， 科学、技术和工业领域的各种环境， 一些基本的研究问题仍然存在。 一个显著 令人关切的是，缺乏完善的方法来处理 不确定性是许多解决问题的任务的特征。为 例如，在医学或地质勘探中，专家必须推断 他们正在调查的过程的状态（例如，患者的 疾病或可能的矿物存款）通过解释间接 通常仅提供部分支持的测量 假设 大多数可用的工具通过以下方式处理这种不确定性： 使用适用性和局限性较差的特别方法 明白 通过开发实用的方法， 概率论在这样的系统，这项研究取代特设 技术与方法，其中的基本假设和 限制可以通过分析来证明，并在 系统建设和使用。

This research investigates pragmatic approaches to computer-based probabilistic reasoning systems. In the past, artificial intelligence researchers have often avoided probability theory for reasoning with uncertainty because of a common perception that the application of probability theory is invariably associated with a commitment to intractable algorithms and inordinate knowledge-acquisition time. The development of efficient algorithmic and assessment techniques would allow investigators to apply a theoretically justified model of belief entailment to complex problems. As a result, the goals in this research are (1) to develop techniques for using knowledge about problem-solving tradeoffs to optimize dynamically the value of computer performance to the user, (2) to construct efficient algorithms for probabilistic reasoning, and (3) to investigate pragmatic techniques for the elicitation of knowledge from experts. Two existing diagnostic systems are testbeds for this research, which is directly applicable to many areas of science and engineering. Although the techniques of artificial intelligence are being used to provide decision support tools ("expert systems") in a variety of settings within science, technology, and industry, several fundamental research problems remain. One significant concern is the lack of well-developed methods for dealing with the uncertainty that characterizes many problem-solving tasks. For example, in medicine or geological exploration, experts must infer the state of the process they are investigating (e.g., a patient's disease or a possible mineral deposit) by interpreting indirect measurements that generally provide only partial support for hypotheses. Most available tools deal with such uncertainty by using ad hoc methods whose applicability and limitations are poorly understood. By developing practical methods for using formal probability theory in such systems, this research replaces ad hoc techniques with approaches for which the underlying assumptions and limitations can be demonstrated analytically and considered during system construction and use.