Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computation and action under bounded resources
Computation and action under bounded resources
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Random Generation of Bayesian Networks
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Belief network algorithms: A study of performance based on domain characterization
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Complexity of probabilistic reasoning in directed-path singly-connected Bayes networks
Artificial Intelligence
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
A bayesian approach to tackling hard computational problems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
An evaluation of machine learning in algorithm selection for search problems
AI Communications - The Symposium on Combinatorial Search
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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The algorithm selection problem aims to select the best algorithm for an input problem instance according to some characteristics of the instance This paper presents a learning-based inductive approach to build a predictive algorithm selection system from empirical algorithm performance data of the Most Probable Explanation(MPE) problem The learned model can serve as an algorithm selection meta-reasoner for the real-time MPE problem Experimental results show that the learned algorithm selection models can help integrate multiple MPE algorithms to gain a better overall performance of reasoning.