Robust Plan Recognition in Dialogue

对话中的稳健计划识别

• 批准号: 8909332
• 负责人: Mary Carberry
• 金额: $ 6.71万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-09-01 至 1992-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8909332&HistoricalAwards=false
• 关键词: Robust; Plan; Recognition; Dialogue

The objective of this project is to develop a more robust process model of plan recognition without the restrictive assumptions of previous systems. In order to effectively engage in a cooperative dialogue, a natural language system must be able to infer a user's plan recognition have made several restrictive assumptions that prevent them from handling naturally occurring dialogues. These assumptions include: 1) a user cannot intend that the system recognize his/her goal unless it is the only goal that can account for the current dialogue in the given situation, and 2) the model inferred by the system is always in total agreement with the task related plan under construction by the user. This project will investigate a more robust model of plan recognition without these unrealistic assumptions and which therefore will be able to handle dialogues that are less than ideal. Our approach will be to study hjow evidential reasoning techniques can be used to facilitate rational and natural recognition of user intention in the presence of multiple alternatives and how techniques for handling ill formed input can be applied to the problem of recognizing and recovering from errors in the system's model of the user's plan.

该项目的目标是开发一个更强大的过程 没有限制性假设的计划识别模型， 以前的系统。 为了有效地参与 合作对话，自然语言系统必须能够 推断用户计划识别已经做出了几个限制 假设，阻止他们处理自然发生的 对话。 这些假设包括：1）用户不能意图 系统识别他/她的目标，除非它是唯一的目标 可以解释在特定情况下当前对话的， 2）系统推断的模型总是总的 与正在施工的任务相关计划的协议 user. 本项目将研究一个更强大的计划模型 没有这些不切实际的假设， 因此，将能够处理的对话， 理想. 我们的方法将是研究hjow证据推理 技术可以用来促进理性和自然 在存在多个 以及处理格式错误的输入的技术如何 适用于识别和恢复的问题 用户计划的系统模型中的错误。