Markov Decision Processes (MDP) have been widely used as a framework for planning under uncertainty. They allow to compute optimal sequences of actions in order to achieve a given...
The algorithm presented here, BCC, is an enhancement of the well known Backtrack used to solve constraint satisfaction problems. Though most backtrack improvements rely on propaga...
Progressing beyond the productivity of present-day languages appears to require using domain-specific knowledge. Domain-specific languages and libraries (DSLs) proliferate, but ...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Given a transportation network having source nodes with evacuees and destination nodes, we want to find a contraflow network configuration (that is, ideal direction for each edge) ...