Project "Agent Architectures for Coping with Uncertainty"
(scroll down for publications)
Autonomous agents need to generate plans that achieve their goals even if
they are never sure what their current state is. For example, the actuator and
sensor uncertainty of mobile robots prevents them from knowing their exact
location during navigation. Consequently, we have designed agent architectures
that are able to cope with a substantial amount of uncertainty. One of these
agent architectures - developed as part of our dissertation - is based on
partially observable Markov decision processes models (POMDP) from operations
research. This agent architecture is part of Xavier, a mobile robot at
Carnegie Mellon University that received navigation requests from users
worldwide via the World Wide Web and has traveled more than 230 kilometers.
- O. Walker, F. Vanegas, F. Gonzalez and S. Koenig. A Deep Reinforcement Learning Framework for UAV Navigation in Indoor Environments. In Proceedings of the IEEE Aerospace Conference (AeroConf), 2019. [downloadable]
- A. Atrash and S. Koenig. Probabilistic Planning for Behavior-Based Robots. In Proceedings of the International FLAIRS Conference (FLAIRS), 531-535, 2001. [downloadable]
- R. Simmons, J. Fernandez, R. Goodwin, S. Koenig and J. O'Sullivan. Lessons Learned from Xavier. IEEE Robotics and Automation Magazine, 7, (2), 33-39, 2000. [downloadable]
- S. Koenig and R.G. Simmons. Xavier: A Robot Navigation Architecture Based on Partially Observable Markov Decision Process Models. In Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, D. Kortenkamp, R. Bonasso and R. Murphy (editor), 91-122. MIT Press, 1998. [downloadable]
- R.G. Simmons, R. Goodwin, K. Haigh, S. Koenig and J. O'Sullivan. Xavier: Experience with a Layered Robot Architecture. Sigart Bulletin, 8, (1-4), 22-33, 1997. [downloadable]
- S. Koenig, R. Goodwin and R.G. Simmons. Robot Navigation with Markov Models: A Framework for Path Planning and Learning with Limited Computational Resources. In Reasoning with Uncertainty in Robotics, L. Dorst, M. van Lambalgen and R. Voorbraak (editor), volume 1093 of Lecture Notes in Artificial Intelligence , 322-337. Springer, 1996. [downloadable]
- R. Simmons, S. Thrun, G. Armstrong, R. Goodwin, K. Haigh, S. Koenig, S. Mahamud, D. Nikovski and J. O'Sullivan. Amelia [Robot Competition Abstract]. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 1368, 1996. [downloadable]
- S. Koenig and R.G. Simmons. Passive Distance Learning for Robot Navigation. In Proceedings of the International Conference on Machine Learning (ICML), 266-274, 1996. [downloadable]
- S. Koenig and R.G. Simmons. Unsupervised Learning of Probabilistic Models for Robot Navigation. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2301-2308, 1996. [downloadable]
- R. Simmons and S. Koenig. Probabilistic Robot Navigation in Partially Observable Environments. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1080-1087, 1995. [downloadable]
Many publishers do not want authors to make their papers available
electronically after the papers have been published. Please use the
electronic versions provided here only if hardcopies are not yet
available. If you have comments on any of these papers, please send me an
email! Also, please send me your papers if we have common interests.
This page was automatically created by a bibliography maintenance system that
was developed as part of an undergraduate research project, advised by Sven Koenig.
Home Page of Sven Koenig