Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions i...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
Bayesian Kullback Ying—Yang dependence reduction system and theory is presented. Via stochastic approximation, implementable algorithms and criteria are given for parameter lear...
We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the r...
— In an effort to ease the burden of programming motor commands for humanoid robots, a computer vision technique is developed for converting a monocular video sequence of human p...
Jeffrey B. Cole, David B. Grimes, Rajesh P. N. Rao
Previous research on the use of coevolution to improve a baseline chess program demonstrated a performance rating of 2550 against Pocket Fritz 2.0 (PF2). A series of 12 games (6 wh...
David B. Fogel, Timothy J. Hays, Sarah L. Hahn, Ja...