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» Learning to Optimize Plan Execution in Information Agents
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ICML
2007
IEEE
16 years 6 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»
15 years 10 months 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»
15 years 10 months 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
15 years 11 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
15 years 10 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