Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Decidability and expressiveness for first-order logics of probability
Information and Computation
Circuits of the mind
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
On the hardness of approximate reasoning
Artificial Intelligence
Extensions of first order logic
Extensions of first order logic
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
A Comparison of New and Old Algorithms for a Mixture EstimationProblem
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Journal of the ACM (JACM)
Worst-case analysis of the perceptron and exponentiated update algorithms
Artificial Intelligence
Machine Learning
Learning to Reason with a Restricted View
Machine Learning
Artificial Intelligence
On the complexity of inference about probabilistic relational models
Artificial Intelligence
Analysis of two gradient-based algorithms for on-line regression
Journal of Computer and System Sciences
A neuroidal architecture for cognitive computation
Journal of the ACM (JACM)
General Convergence Results for Linear Discriminant Updates
Machine Learning
Machine Learning
Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
The Robustness of the p-Norm Algorithms
Machine Learning
Reasoning about Uncertainty
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
First order LUB approximations: characterization and algorithms
Artificial Intelligence - Special volume on reformulation
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
On probabilistic inference by weighted model counting
Artificial Intelligence
The backdoor key: a path to understanding problem hardness
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Compiling relational Bayesian networks for exact inference
International Journal of Approximate Reasoning
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Solving #SAT using vertex covers
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
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A recurrent problem in the development of reasoning agents is how to assign degrees of beliefs to uncertain events in a complex environment. The standard knowledge representation framework imposes a sharp separation between learning and reasoning; the agent starts by acquiring a "model" of its environment, represented into an expressive language, and then uses this model to quantify the likelihood of various queries. Yet, even for simple queries, the problem of evaluating probabilities from a general purpose representation is computationally prohibitive. In contrast, this study embarks on the learning to reason (L2R) framework that aims at eliciting degrees of belief in an inductive manner. The agent is viewed as an anytime reasoner that iteratively improves its performance in light of the knowledge induced from its mistakes. Indeed, by coupling exponentiated gradient strategies in learning and weighted model counting techniques in reasoning, the L2R framework is shown to provide efficient solutions to relational probabilistic reasoning problems that are provably intractable in the classical paradigm.