ARCO1: an application of belief networks to the oil market
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Using hundreds of workstations to solve first-order logic problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Structure and chance: melding logic and probability for software debugging
Communications of the ACM
Applying Bayesian networks to information retrieval
Communications of the ACM
Decision-theoretic troubleshooting
Communications of the ACM
Bayesian Network Refinement Via Machine Learning Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nagging: A Distributed, Adversarial Search-Pruning Technique Applied to First-Order Inference
Journal of Automated Reasoning
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A Novel Asynchronous Parallelism Scheme for First-Order Logic
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Exploring parallelism in learning belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An optimal multiprocessor combinatorial auction solver
Computers and Operations Research
A parallel algorithm for learning Bayesian networks
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Automatic construction of bayesian network structures by means of a concurrent search mechanism
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
IMS 10-Validation of a co-evolving diagnostic algorithm for evolvable production systems
Engineering Applications of Artificial Intelligence
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We present a new distributed algorithm for computing the minimum description length (MDL) in learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric and a distributed, asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault-tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, effectiveness, and scalability of our approach empirically with several experiments using networked machines. More specifically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables.