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

本研究探讨了基于计算机的概率推理系统的语用方法。过去，人工智能研究人员经常避免使用概率理论进行不确定性推理，因为人们普遍认为，概率理论的应用总是与对难以处理的算法的承诺和获取知识的时间过长有关。高效算法和评估技术的发展将使研究人员能够将理论上合理的信念蕴涵模型应用于复杂的问题。因此，本研究的目标是(1)开发利用问题解决权衡的知识来动态优化计算机性能对用户的价值的技术；(2)构建有效的概率推理算法；(3)研究从专家那里获取知识的实用技术。现有的两个诊断系统是本研究的试验台，直接适用于许多科学和工程领域。尽管人工智能技术正被用于在科学、技术和工业的各种环境中提供决策支持工具(“专家系统”)，但仍有几个基本研究问题。一个重要的问题是缺乏完善的方法来处理不确定性，这种不确定性是许多问题解决任务的特征。例如，在医学或地质勘探中，专家必须通过解释通常只为假设提供部分支持的间接测量来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藏)。大多数可用的工具通过使用专门的方法来处理这种不确定性，而这些方法的适用性和局限性还没有得到很好的理解。通过开发在这种系统中使用形式概率理论的实用方法，本研究用一些方法取代了特别的技术，这些方法的潜在假设和限制可以在系统构建和使用期间进行分析论证和考虑。

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.