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UAI
1998
13 years 6 months ago
Learning From What You Don't Observe
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algor...
Mark A. Peot, Ross D. Shachter
TIME
1994
IEEE
13 years 9 months ago
Belief Revision in a Discrete Temporal Probability-Logic
We describe a discrete time probabilitylogic for use as the representation language of a temporal knowledge base. In addition to the usual expressive power of a discrete temporal ...
Scott D. Goodwin, Howard J. Hamilton, Eric Neufeld...
AGENTS
2001
Springer
13 years 9 months ago
Monitoring deployed agent teams
Recent years are seeing an increasing need for on-line monitoring of deployed distributed teams of cooperating agents, e.g., for visualization, or performance tracking. However, i...
Gal A. Kaminka, David V. Pynadath, Milind Tambe
ICRA
2010
IEEE
136views Robotics» more  ICRA 2010»
13 years 2 months ago
Efficient planning under uncertainty for a target-tracking micro-aerial vehicle
A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its u...
Ruijie He, Abraham Bachrach, Nicholas Roy
IDT
2008
123views more  IDT 2008»
13 years 5 months ago
A rich semantic model of track as a foundation for sharing beliefs regarding dynamic objects and events
: Many defense, homeland security, and commercial security objectives require continuous tracking of mobile entities such as aircraft. The systems that perform these functions prod...
Frederick Hayes-Roth, Curtis L. Blais