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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 7 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
NIPS
1993
15 years 5 months ago
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming
Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have ...
Christopher G. Atkeson
SIGSOFT
1998
ACM
15 years 8 months ago
Effective Whole-Program Analysis in the Presence of Pointers
Understanding large software systems is difficult. Traditionally, automated tools are used to assist program understanding. However, the representations constructed by these tool...
Darren C. Atkinson, William G. Griswold
RTSS
2006
IEEE
15 years 10 months ago
Generalized Elastic Scheduling
The elastic task model proposed by Buttazzo, et. al. [9] is a powerful model for adapting real-time systems in the presence of uncertainty. This paper generalizes the existing ela...
Thidapat Chantem, Xiaobo Sharon Hu, Michael D. Lem...
KR
1992
Springer
15 years 8 months ago
Intelligent Backtracking Techniques for Job Shop Scheduling
This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within non-relaxable time windows i.e. earliest latest possible start...
Yalin Xiong, Norman M. Sadeh, Katia P. Sycara