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 ...
Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves bette...
This paper presents ReVive, a novel general-purpose rollback recovery mechanism for shared-memory multiprocessors. ReVive carefully balances the conflicting requirements of avail...
Enabling video transport over ad-hoc networks is more challenging than over other wireless networks because a connection path in an ad-hoc network is highly error-prone and a path...
Shunan Lin, Yao Wang, Shiwen Mao, Shivendra S. Pan...