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Spatial and Temporal Abstractions in POMDPs Applied to Robot Navigation
(2005-09-27)
Partially observable Markov decision processes (POMDPs) are a well studied paradigm for programming autonomous robots, where the robot sequentially chooses actions to achieve long term goals efficiently. Unfortunately, ...
Mobilized ad-hoc networks: A reinforcement learning approach
(2003-12-04)
Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. ...
Combining dynamic abstractions in large MDPs
(2004-10-21)
One of the reasons that it is difficult to plan and act in real-worlddomains is that they are very large. Existing research generallydeals with the large domain size using a static representation andexploiting a single ...
Learning object segmentation from video data
(2003-09-08)
This memo describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the ...
Learning object segmentation from video data
(2003-09-08)
This memo describes the initial results of a project to create aself-supervised algorithm for learning object segmentation from videodata. Developmental psychology and computational experience havedemonstrated that the ...
Learning with Deictic Representation
(2002-04-10)
Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be ...
Mobilized ad-hoc networks: A reinforcement learning approach
(2003-12-04)
Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. ...