Given a high-level specification and a low-level programming language, our goal is to automatically synthesize an efficient program that meets the specification. In this paper,...
Shachar Itzhaky, Sumit Gulwani, Neil Immerman, Moo...
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
In this paper, we develop models for adjusting or setting fares on a transit system to encourage passengers to choose travel strategies that lead to the least travel delay for the...
We used in the past a lot of computational power and human expertise for having a very big dataset of good 9x9 Go games, in order to build an opening book. We improved a lot the al...
We consider the problem of planning in a stochastic and discounted environment with a limited numerical budget. More precisely, we investigate strategies exploring the set of poss...