The expected-outcome model of two-player games
The expected-outcome model of two-player games
Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fast probabilistic modeling for combinatorial optimization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A sampling-based heuristic for tree search applied to grammar induction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Differencing Method of Set Partitioning
The Differencing Method of Set Partitioning
Optimal search protocols
Interleaved depth-first search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
From approximate to optimal solutions: a case study of number partitioning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improved limited discrepancy search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Worst-case quadratic loss bounds for prediction using linear functions and gradient descent
IEEE Transactions on Neural Networks
Principled Exploitation of Heuristic Information
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Learning How to Propagate Using Random Probing
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Beam-ACO for the travelling salesman problem with time windows
Computers and Operations Research
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Heuristic search applied to abstract combat games
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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When not enough time is available to fully explore a search tree, different algorithms will visit different leaves. Depth-first search and depth-bounded discrepancy search, for example, make opposite assumptions about the distribution of good leaves. Unfortunately, it is rarely clear a priori which algorithm will be most appropriate for a particular problem. Rather than fixing strong assumptions in advance, we propose an approach in which an algorithm attempts to adjust to the distribution of leaf costs in the tree while exploring it. By sacrificing completeness, such flexible algorithms can exploit information gathered during the search using only weak assumptions. As an example, we show how a simple depth-based additive cost model of the tree can be learned on-line. Empirical analysis using a generic tree search problem shows that adaptive probing is competitive with systematic algorithms on a variety of hard trees and outperforms them when the node-ordering heuristic makes many mistakes. Results on boolean satisfiability and two different representations of number partitioning confirm these observations. Adaptive probing combines the flexibility and robustness of local search with the ability to take advantage of constructive heuristics.