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CORR
2010
Springer
105views Education» more  CORR 2010»
14 years 12 months ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...
JAIR
2008
107views more  JAIR 2008»
15 years 1 months ago
Planning with Durative Actions in Stochastic Domains
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
Mausam, Daniel S. Weld
DGO
2006
82views Education» more  DGO 2006»
15 years 2 months ago
Should e-government design for citizen participation?: stealth democracy and deliberation
Cyberoptimists have heralded an age of citizen engagement enabled by electronic technologies that allow widespread citizen input in government decision making. In contrast, influe...
Peter Muhlberger
WEBI
2005
Springer
15 years 6 months ago
Providing Expert Advice by Analogy for On-Line Help
One of the principal problems of online help is the mismatch between the specialized knowledge and technical vocabulary of experts who are providing the help, and the relative na...
Henry Lieberman, Ashwani Kumar
ICANN
2009
Springer
15 years 8 months ago
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning
Model complexity is key concern to any artificial learning system due its critical impact on generalization. However, EC research has only focused phenotype structural complexity ...
Faustino J. Gomez, Julian Togelius, Jürgen Sc...