Algorithms in combinatorial geometry
Algorithms in combinatorial geometry
On the sum of squares of cell complexities in hyperplane arrangements
SCG '91 Proceedings of the seventh annual symposium on Computational geometry
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Journal of the ACM (JACM)
Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
Evaluating Search Heuristics and Optimization Techniques in Propositional Satisfiability
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Combining multiple heuristics online
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A constraint satisfaction framework for executing perceptions and actions in diagrammatic reasoning
Journal of Artificial Intelligence Research
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
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In this work we introduce and study the question of combining multiple heuristics. Given a problem instance, each of the multiple heuristics is capable of computing the correct solution, but has a different cost. In our models the user executes multiple heuristics until one of them terminates with a solution. Given a set of problem instances, we show how to efficiently compute an optimal fixed schedule for a constant number of heuristics, and show that in general, the problem is computationally hard even to approximate (to within a constant factor). We also discuss a probabilistic configuration, in which the problem instances are drawn from some unknown fixed distribution, and show how to compute a near optimal schedule for this setup.