Presidential Young Investigator Award

智能评估相关性是人工智能的核心问题。无论是在诊断、解决问题还是解释方面，人工智能系统都必须确定数据库中与任务相关的信息。评估相关性是困难的，因为它取决于背景。这项工作专注于以一种易于计算的方式对上下文中案例或特征的相关性进行符号化推理。将扩展领域理论整合到相关性评估中是这项工作的主要目标，其中此类理论往往定义不清或不全面。这种方法的最终目标是设计一个智能辅导系统，训练学生与案例辩论的能力。

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.