CAREER: Integrating Multiple Knowledge Sources for Robust Human-Machine Communication

这项拟议的研究旨在调查能够支持人与计算机系统之间强大而灵活的交互的对话模型。当计算机被用来教授用户一个新的主题或技术时，用户可能难以理解系统答案背后的知识和推理。为了解决这一困难，这项研究将指定一种计算理论和方法，以允许系统动态地改变材料的呈现方式。具体地说，该项目将解决两个重要的研究问题。首先，它将提供语言、意图和社会信息的规范和表示，这些信息影响人们对正在进行的对话的理解，并允许在发生误解时检测到误解。其次，研究将提供整合和利用这些信息源的方法，以产生相关的话语，并在出现交际问题时识别和解决它们。对该方法的评估将使用两个正在开发的辅导系统：一个解释医学概念，另一个教授一般生活技能，如良好的营养和预算。对拟议研究的最终评估将基于对最终用户群体、运行时性能和技术正确性的测试。

The proposed research aims to intestigate models of dialogue that can support robust and flexible interaction between people and computer systems. When computers are used to teach users about a new topic or technique, users may have difficulty understanding the knowledge and reasoning behind the system's answers. To address this difficulty, this research will specify a computational theory and methods to allow systems to vary the presentation of material dynamically. Specifically, this project will address two important research issues. First, it will provide a specification and representation of the linguistic, intentional, and social information that influence people's understanding of an ongoing dialog and that allow for the detection of misunderstanding, if it should arise. Second, the research will provide methods for integrating and employing these information sources to produce relevant utterances and to identify and resolve communication problems as they arise. Evaluation of the approach will employ two tutoring systems that are under development: one that explains medical concepts and one that teaches general life skills, such as good nutrition and budgeting. Final evaluation of the proposed research will be based on testing with end-user populations, runtime performance, and technical correctness.