Presidential Young Investigator Award

智能地评估相关性是AI的核心问题。 无论是诊断、解决问题还是解释，人工智能系统 必须确定数据库中的信息， 任务。 评估相关性是困难的，因为它取决于 上下文 这项工作的重点是象征性地共鸣， 计算环境中的情况或特征的相关性 温顺的方式。 整合扩展域理论，其中， 理论往往是不明确的或片面的，到评估 相关性是这项工作的主要目标。 的最终目标 这种方法是设计一个智能辅导系统， 将训练学生用案例进行辩论。

Intelligently assessing relevance is a central issue in AI. Whether in diagnosis, problem solving, or explanation, AI systems must determine the information in a database that isrelevant to the task. Assessing relevance is difficult because it depends on context. This work focuses on resoning symbolically about relevance of cases or features in context in a computationally tractable way. Integrating expanded domain theories, where such theories are often ill-defined or partial, into the assessment of relevance is a primary goal of the work. The ultimate goal of this approach is to design an intelligent tutoring system that will train students to argue with cases.