Effective Intention Recognition

下一代智能系统将需要意图识别，识别目标并预测人和其他代理的未来行为的能力。本项目旨在为实时有效的意图识别开发新的理论和实用技术。这项工作的一个新的方面是使用基于语料库的统计模型，在过去的十年中，在自然语言处理。然而，该项目中最具革命性的发展是将意图识别技术从基于固定计划的模型推广到基于问题解决的模型。实际上，所有以前的工作都假设代理人正在执行预先确定的计划。然而，在实际操作中，智能体形成计划，执行一些行动，评估情况，修改计划，甚至放弃计划并采用新的计划。我们正在定义一个新的形式主义的意图识别的基础上观察到的问题解决行为，将消除一个主要的障碍，在使用意图识别在真实的application.As计算机成为一个越来越普遍的一部分，我们的社会结构和计算机变得更加强大，计算机接口仍然是十多年前。这越来越增加了复杂性和人类用户的负担。有效的实时意图识别能力将使新一代的计算机接口能够使计算机以方便人们的方式行事，而不是迫使人们以方便计算机的方式行事。

The next generation of intelligent systems will require intention recognition, the ability to identify goals and predict future behavior of people and other agents. This project is developing new theories and practical techniques for real-time effective intention recognition. A novel aspect of this work is the use of corpus-based statistical models that have been developed over the last decade in natural language processing. The most revolutionary development in the project, however, is the generalization of intention recognition techniques from fixed plan based models to problem-solving based models. Virtually all prior work makes the assumption that agents are executing plans that are fixed in advance. In actual practice, however, agents form plans, execute a few actions, assess the situation, revise their plans, and even abandon plans and adopt new ones. We are defining a new formalism for intention recognition based on observed problem solving behavior that will remove a major obstacle in using intention recognition in real applications.As computers become an ever more pervasive part of the fabric of our society and computers become dramatically more powerful, computer interfaces remain much as they were over a decade ago. This increasingly adds complexity and burdens human users. Effective real-time intention recognition capabilities will enable a new generation of computer interfaces that make computers act in ways convenient for people rather than forcing people to act in ways convenient for computers.