Marginalizing out future passengers in group elevator control
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Certainty closure: Reliable constraint reasoning with incomplete or erroneous data
ACM Transactions on Computational Logic (TOCL)
Improving Architecture-Based Self-Adaptation through Resource Prediction
Software Engineering for Self-Adaptive Systems
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Waiting and relocation strategies in online stochastic vehicle routing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Online stochastic optimization in the large: application to kidney exchange
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Realtime online solving of quantified CSPs
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Online stochastic and robust optimization
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Online stochastic reservation systems
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
An event-driven optimization framework for dynamic vehicle routing
Decision Support Systems
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This paper considers online stochastic optimization problems where time constraints severely limit the number of offline optimizations which can be performed at decision time and/or in between decisions. It proposes a novel approach which combines the salient features of the earlier approaches: the evaluation of every decision on all samples (expectatio0n) and the ability to avoid distributing the samples among decisions (consensus). The key idea underlying the novel algorithm is to approximate the regret of a decision d. The regret algorithm is evaluated on two fundamentally different applications: online packet scheduling in networks and online multiple vehicle routing with time windows. On both applications, it produces significant benefits over prior approaches.