RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
This paper addresses the problem of stochastic task execution time estimation agnostic to the process distributions. The proposed method is orthogonal to the application structure ...
Current practice of Web site development does not address explicitly the problems related to multilingual sites. The same information, as well as the same navigation paths, page f...
Paolo Tonella, Filippo Ricca, Emanuele Pianta, Chr...
Most online evolution of application depends on its runtime environment. This paper addresses how to support online evolution by application server, which is considered as third k...
This paper presents ReVive, a novel general-purpose rollback recovery mechanism for shared-memory multiprocessors. ReVive carefully balances the conflicting requirements of avail...