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ICML
2006
IEEE
16 years 7 months ago
An intrinsic reward mechanism for efficient exploration
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
Özgür Simsek, Andrew G. Barto
WWW
2006
ACM
16 years 7 months ago
Tables and trees don't mix (very well)
There are principal differences between the relational model and XML's tree model. This causes problems in all cases where information from these two worlds has to be brought...
Erik Wilde
ICCAD
2006
IEEE
128views Hardware» more  ICCAD 2006»
16 years 3 months ago
Improvements to combinational equivalence checking
The paper explores several ways to improve the speed and capacity of combinational equivalence checking based on Boolean satisfiability (SAT). State-of-the-art methods use simulat...
Alan Mishchenko, Satrajit Chatterjee, Robert K. Br...
ICDAR
2009
IEEE
16 years 1 months ago
Combination of Measurement-Level Classifiers: Output Normalization by Dynamic Time Warping
Classifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system str...
Giuseppe Pirlo, Donato Impedovo, Claudia Adamita T...
ICANN
2009
Springer
16 years 1 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...