Learning hierarchical models of activity

This paper investigates learning hierarchical statistical activity models in indoor environments. The abstract hidden Markov model (AHMM) is used to represent behaviors in stochastic environments. We train the model using both labeled and unlabeled data and estimate the parameters using expectation maximization (EM). Results are shown on three datasets: data collected in lab, entryway, and home environments. The results show that hierarchical models outperform flat models.

[1]  Suchi Saria,et al.  Probabilistic Plan Recognition in Multiagent Systems , 2004, ICAPS.

[2]  William Whittaker,et al.  Conditional particle filters for simultaneous mobile robot localization and people-tracking , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[3]  Maja J. Mataric,et al.  A laser-based people tracker , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[4]  Universityof SouthernCalifornia LosAngeles.  Laser-based People Tracking , 2002 .

[5]  Sebastian Thrun,et al.  Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[6]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[7]  Adnan Darwiche,et al.  Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..

[8]  Svetha Venkatesh,et al.  Policy Recognition in the Abstract Hidden Markov Model , 2002, J. Artif. Intell. Res..

[9]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[10]  Maja J. Mataric,et al.  General spatial features for analysis of multi-robot and human activities from raw position data , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Wolfram Burgard,et al.  Using EM to learn motion behaviors of persons with mobile robots , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .