Liao Lin, Patterson Donald J, Fox Dieter, Kautz Henry
Department of Computer Science & Engineering, University of Washington, Seattle, Washington 98195, USA.
Ann N Y Acad Sci. 2006 Dec;1093:249-65. doi: 10.1196/annals.1382.017.
In this article we discuss an assisted cognition information technology system that can learn personal maps customized for each user and infer his daily activities and movements from raw GPS data. The system uses discriminative and generative models for different parts of this task. A discriminative relational Markov network is used to extract significant places and label them; a generative dynamic Bayesian network is used to learn transportation routines, and infer goals and potential user errors at real time. We focus on the basic structures of the models and briefly discuss the inference and learning techniques. Experiments show that our system is able to accurately extract and label places, predict the goals of a person, and recognize situations in which the user makes mistakes, such as taking a wrong bus.
在本文中，我们讨论了一种辅助认知信息技术系统，该系统可以学习为每个用户定制的个人地图，并从原始GPS数据中推断出其日常活动和行动轨迹。该系统针对此任务的不同部分使用了判别模型和生成模型。判别关系马尔可夫网络用于提取重要地点并对其进行标注；生成动态贝叶斯网络用于学习出行路线，并实时推断目标和潜在的用户错误。我们重点关注模型的基本结构，并简要讨论推理和学习技术。实验表明，我们的系统能够准确地提取和标注地点、预测人的目标，并识别用户犯错的情况，比如坐错公交车。
