Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with...
The success ofreinforcement learninginpractical problems depends on the ability to combine function approximation with temporal di erence methods such as value iteration. Experime...
We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and design an algorithm to solve S-MCKP and the ...
This paper presents R-U-In? ? a social networking application that leverages Web 2.0 and IMS-based Converged Networks technologies to create a rich next-generation service. R-U-In...
We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the we...