HAILFINDER: Bayesian Prediction of Severe Weather

研究人员计划开发HAILFINDER， 一个基于知识的系统，用于预报恶劣天气， 科罗拉多东北部。 直到最近， 系统基于生产规则、框架或 各种非标准逻辑。 第四种方法是现在 使用分层贝叶斯推理和其他 相关技术来进行诊断，得出结论， 回答问题并做出预测。 HAILFINDER将成为 这是第一个为自然科学领域设计的系统。 其目的将是比较规范的性能 设计了基于知识的系统和基于规则的系统 系统和专家的表现。 美国国家海洋 大气管理局（NOAA）将收集和 提供广泛的天气信息，并为专家提供 在预测严重气候变化的系统之间 它将在1992年夏天进行。 HAILFINDER的研究团队由四名 调查人员谁体现了各种技能， 以满足这项任务的需要。 该项目 使用特定天气应用程序的潜力， 产生具有重大科学意义的结果。 甚至 如果只是部分成功，它承诺提供一个更 使用概率专家系统的坚实基础， 实用工具。

The investigators plan to develop HAILFINDER, a knowledge-based system for forecasting severe weather in Northeastern Colorado. Until recently most knowledge-based systems have been based on production rules, frames, or various non-standard logics. A fourth approach is now emerging which uses hierarchical Bayesian inference and other related techniques to perform diagnoses, draw conclusions, answer questions, and produce forecasts. HAILFINDER will be the first such system designed for a natural science domain. Its purpose will be to compare the performance of normatively designed knowledge-based systems with that of rule-based systems and with expert performance. The National Oceanic and Atmospheric Administration (NOAA) will collect and provide extensive weather information and supply the experts for a competition among systems for forecasting severe weather that it will conduct in the summer of 1992. HAILFINDER's research team is comprised of four investigators who embody a variety of skills that are well suited to meeting the needs of this task. The project has the potential of using the specific weather application to yield results of substantial scientific significance. Even if only partially successful, it promises to provide a more solid footing for the use of probabilistic expert systems as practical tools.