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» Learning to Optimize Plan Execution in Information Agents
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ICML
2007
IEEE
14 years 8 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
CDC
2009
IEEE
168views Control Systems» more  CDC 2009»
14 years 5 days ago
Distributed coverage games for mobile visual sensors (II) : Reaching the set of global optima
— We formulate a coverage optimization problem for mobile visual sensor networks as a repeated multi-player game. Each visual sensor tries to optimize its own coverage while mini...
Minghui Zhu, Sonia Martínez
CDC
2009
IEEE
178views Control Systems» more  CDC 2009»
14 years 5 days ago
Distributed coverage games for mobile visual sensors (I): Reaching the set of Nash equilibria
— We formulate a coverage optimization problem for mobile visual sensor networks as a repeated multi-player game. Each visual sensor tries to optimize its own coverage while mini...
Minghui Zhu, Sonia Martínez
ITICSE
2005
ACM
14 years 1 months ago
Iconic programming for flowcharts, java, turing, etc
One of the largest barriers to learning programming is the precise and complex syntax required to write programs. This barrier is a key impediment to the integration of programmin...
Stephen Chen, Stephen Morris
IDA
1999
Springer
13 years 11 months ago
Reasoning about Input-Output Modeling of Dynamical Systems
The goal of input-output modeling is to apply a test input to a system, analyze the results, and learn something useful from the causeeffect pair. Any automated modeling tool that...
Matthew Easley, Elizabeth Bradley