Presidential Young Investigator Award

智能地评估相关性是人工智能的核心问题。无论是诊断、解决问题还是解释，人工智能系统都必须在数据库中确定与任务相关的信息。评估相关性是困难的，因为它取决于上下文。这项工作的重点是用一种计算上可处理的方式象征性地推理上下文中案例或特征的相关性。将扩展的领域理论（这些理论通常定义不清或不完整）整合到相关性评估中是本工作的主要目标。这种方法的最终目标是设计一个智能辅导系统，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.