Sound and Efficient Inference with Probabilistic and Deterministic Dependencies

By Hoifung Poon, Pedro M. Domingos. AAAI Conference on Artificial Intelligence, 2006

302 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/865c4b3e6effc5c83ac1ae6850c16ef8d9b930ee

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Citations in the corpus

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior (2013, background): “(Poon and Domingos 2006) introduce statistical sampling to improve the efficiency of search for satisfiability testing.”
Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic (2012, unknown): “Our system produces a list of all test instances, ordered by marginal probability as computed by MC-SAT.”
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011, methodology): “The state-of-the-art marginal inference algorithm is MC-SAT [38], which is implemented in both Alchemy and Tuffy .”
The Alchemy System for Statistical Relational AI: User Manual (2007, methodology): “If you are not already familiar with Markov logic, we recommend that you read the papers Markov Logic Networks [7], Discriminative Training of Markov Logic Networks [9], Learning the Structure of Markov Logic Networks [3], Memory-Efficient Inference in Relational Domains [10] and Sound and Efficient”
A Review of Relational Machine Learning for Knowledge Graphs (2015, methodology): “One approach for computing posterior marginals is to use Gibbs sampling (see, or example, [31, 127]) or MC-SAT [128].”
Hybrid Markov Logic Networks (2008, methodology): “We used the best settings for simulated tempering found by Poon and Domingos (2006).”
Statistical predicate invention (2007, methodology): “Since in each run these are only 10% of the training set, setting them to false does not greatly change the sufficient statistics (true clause counts) learning is based on. We then ran MC-SAT ( Poon & Domingos, 2006 ) on the MLNs learned by MSL to infer the probabilities of the test atoms.”
Mapping and Revising Markov Logic Networks for Transfer Learning (2007, methodology): “We used the MC-SAT inference algorithm ( Poon & Domingos 2006 ) and, like Kok and Domingos (2005), tested for the gliterals of each of the predicates of the domain in turn, providing the rest as evidence, and averaging over the results.”
Joint Inference in Information Extraction (2007, methodology): “In particular, we performed inference using the MC-SAT algorithm ( Poon & Domingos 2006 ), and discriminative weight learning using the voted perceptron algorithm (Singla & Domingos 2005).”
Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior (2015, background): “Poon and Domingos (2006) proposed statistical sampling to improve searching eﬃciency for satisﬁability testing.”
Unsupervised Semantic Parsing (2009, methodology): “Even an efﬁcient sampler like MC-SAT (Poon and Domingos, 2006), as used in Poon & Domingos (2008), would have a hard time generating accurate estimates within a reasonable amount of time.”
Automatically refining the wikipedia infobox ontology (2008, methodology): “Discriminative learning is used to determine the weights of formulas [30], and MC-SAT is used for inference [27].”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “[108] proposed statistical sampling to improve searching efficiency for satisfiability testing.”
Probabilistic (logic) programming concepts (2015, methodology): “Fierens et al (2014) have used MC-SAT (Poon and Domingos, 2006) to perform approximate WMC on the CNF representing all models.”
Under Consideration for Publication in Theory and Practice of Logic Programming Inference and Learning in Probabilistic Logic Programs Using Weighted Boolean Formulas (methodology): “In terms of expressivity, Markov Logic has the drawback that it cannot express (nonground ) inductive definitions.”