Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
The algorithmic framework developed for improving heuristic solutions of the new version of deterministic TSP [Choi et al., 2002] is extended to the stochastic case. To verify the...
Compression in column-oriented databases has been proven to offer both performance enhancements and reductions in storage consumption. This is especially true for read access as c...
— We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic w...
This article describes a new model of probability density function and its use in estimation of distribution algorithms. The new model, the distribution tree, has interesting prope...