Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
We set out to answer a question we were asked by software project management: how much effort remains to be spent on a specific software project and how will that effort be distri...
Globus has become a standard in the construction of Grid computing environments. However, it still needs more work and research to satisfy requirements from various grid applicatio...
Borodin, Nielsen and Rackoff [5] proposed a framework for ing the main properties of greedy-like algorithms with emphasis on scheduling problems, and Davis and Impagliazzo [6] ext...
Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applie...