Database system concepts
Principles of artificial intelligence
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Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Valuation-based systems for Bayesian decision analysis
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Probabilistic Horn abduction and Bayesian networks
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Answering queries from context-sensitive probabilistic knowledge bases
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Probabilistic frame-based systems
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Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
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Chaff: engineering an efficient SAT solver
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
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UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
New advances in inference by recursive conditioning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Value elimination: bayesian inference via backtracking search
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
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Robotics and Autonomous Systems
Plan assessment for autonomous manufacturing as Bayesian inference
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SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Importance sampling on Bayesian networks with deterministic causalities
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
From Bayesian notation to pure racket via discrete measure-theoretic probability in λZFC
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KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user-interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model.