
Learning Branching Heuristics for Propositional Model Counting
Propositional model counting or #SAT is the problem of computing the num...
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Exploring StrategyProofness, Uniqueness, and Pareto Optimality for the Stable Matching Problem with Couples
The Stable Matching Problem with Couples (SMPC) is a ubiquitous realwo...
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Solving #SAT and Bayesian Inference with Backtracking Search
Inference in Bayes Nets (BAYES) is an important problem with numerous ap...
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Probability Distributions Over Possible Worlds
In Probabilistic Logic Nilsson uses the device of a probability distribu...
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Lp : A Logic for Statistical Information
This extended abstract presents a logic, called Lp, that is capable of r...
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Using Causal Information and Local Measures to Learn Bayesian Networks
In previous work we developed a method of learning Bayesian Network mode...
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Using FirstOrder Probability Logic for the Construction of Bayesian Networks
We present a mechanism for constructing graphical models, specifically B...
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Using New Data to Refine a Bayesian Network
We explore the issue of refining an existent Bayesian network structure ...
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Generating New Beliefs From Old
In previous work [BGHK92, BGHK93], we have studied the randomworlds app...
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UCPNetworks: A Directed Graphical Representation of Conditional Utilities
We propose a new directed graphical representation of utility functions,...
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Value Elimination: Bayesian Inference via Backtracking Search
Backtracking search is a powerful algorithmic paradigm that can be used ...
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Proceedings of the TwentyFirst Conference on Uncertainty in Artificial Intelligence (2005)
This is the Proceedings of the TwentyFirst Conference on Uncertainty in...
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Towards Completely Lifted Searchbased Probabilistic Inference
The promise of lifted probabilistic inference is to carry out probabilis...
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Reasoning about Noisy Sensors and Effectors in the Situation Calculus
Agents interacting with an incompletely known world need to be able to r...
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