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ICML
2007
IEEE
16 years 5 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
COMCOM
2007
100views more  COMCOM 2007»
15 years 4 months ago
Sensor replacement using mobile robots
Sensor replacement is important for sensor networks to provide continuous sensing services. Upon sensor node failures, holes (uncovered areas) may appear in the sensing coverage. ...
Yongguo Mei, Changjiu Xian, Saumitra M. Das, Y. Ch...
EUROPAR
2005
Springer
15 years 10 months ago
Batch-Scheduling Dags for Internet-Based Computing
The process of scheduling computations for Internet-based computing presents challenges not encountered with more traditional platforms for parallel and distributed computing. The...
Grzegorz Malewicz, Arnold L. Rosenberg
IWNAS
2008
IEEE
15 years 11 months ago
POND: The Power of Zone Overlapping in DHT Networks
— Distributed Hash Table (DHT) based algorithms have been proposed to solve the routing issues in large-scale Peer-to-Peer (P2P) applications. DHT systems create an elegant Peer-...
Zhiyong Xu, Jizhong Han, Laxmi N. Bhuyan
ISPDC
2008
IEEE
15 years 11 months ago
Performance Analysis of Grid DAG Scheduling Algorithms using MONARC Simulation Tool
This paper presents a new approach for analyzing the performance of grid scheduling algorithms for tasks with dependencies. Finding the optimal procedures for DAG scheduling in Gr...
Florin Pop, Ciprian Dobre, Valentin Cristea