Online computation and competitive analysis
Online computation and competitive analysis
Computational Optimization and Applications
Least-squares policy iteration
The Journal of Machine Learning Research
Parametric query optimization for linear and piecewise linear cost functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the use of integer programming models in AI planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multiconsistency and robustness with global constraints
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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We address the following dilemma: When making decisions in real life, we often face the problem that, while we have time to contemplate about a problem, we are not entirely sure what the exact parameters of our problem will be. And, on the other hand, as soon as the real world is revealed to us, we need to act quickly and have no more time to rethink our actions extensively. We suggest an approach that allows to trade uncertainty for time and marginal quality loss and discuss its applicability to combinatorial optimization problems that can be formulated as linear and integer linear programs. The core idea consists in solving a polynomial number of problems in the extensive time period before the day of operation, so that, as soon as complete information is available, a feasible near-optimal solution to the problem can be found in sublinear time.