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Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
Abstract

Cited by 816 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a firstorder formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
Probabilistic reasoning with answer sets
 In Proceedings of LPNMR7
, 2004
"... Abstract. We give a logic programming based account of probability and describe a declarative language Plog capable of reasoning which combines both logical and probabilistic arguments. Several nontrivial examples illustrate the use of Plog for knowledge representation. 1 ..."
Abstract

Cited by 91 (11 self)
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Abstract. We give a logic programming based account of probability and describe a declarative language Plog capable of reasoning which combines both logical and probabilistic arguments. Several nontrivial examples illustrate the use of Plog for knowledge representation. 1
Firstorder probabilistic models for coreference resolution
 In HLT/NAACL
, 2007
"... Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a firstorder probabilistic model for coreference. We outline a set of approximat ..."
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Cited by 86 (20 self)
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Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a firstorder probabilistic model for coreference. We outline a set of approximations that make this approach practical, and apply our method to the ACE coreference dataset, achieving a 45 % error reduction over a comparable method that only considers features of pairs of noun phrases. This result demonstrates an example of how a firstorder logic representation can be incorporated into a probabilistic model and scaled efficiently. 1
Learning and Inference in WEIGHTED LOGIC WITH APPLICATION TO NATURAL LANGUAGE PROCESSING
, 2008
"... ..."
Synthesis of tiled patterns using factor graphs
 ACM Trans. Graph
, 2013
"... Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to specify valid tile arrangements and to emphasize ..."
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Cited by 11 (1 self)
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Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to specify valid tile arrangements and to emphasize multitile structures. We model a pattern as a probabilistic graphical model called a factor graph. Factors represent the hard logical constraints between tiles, the soft statistical relationships that determine style, and the local dependencies between tiles at neighboring sites. We describe a simple method for learning factor functions from a small exemplar. We then synthesize new patterns through a stochastic search method that is inspired by MCSAT. Efficient synthesis is challenging because of the combination of hard and soft constraints. Our synthesis algorithm, called BLOCKSS, scales linearly with the number of tiles and the hardness of the problem. We use our technique to model building facades, cities, and decorative patterns.
FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. THIS PUBLICATION COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS. CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN. Commentary
, 2008
"... The material in the C99 subsections is copyright © ISO. The material in the C90 and C++ sections that is quoted from the respective language standards is copyright © ISO. Credits and permissions for quoted material is given where that material appears. ..."
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The material in the C99 subsections is copyright © ISO. The material in the C90 and C++ sections that is quoted from the respective language standards is copyright © ISO. Credits and permissions for quoted material is given where that material appears.
Using the Probabilistic Logic Programming Language Plog for Causal and Counterfactual Reasoning and Nonnaive Conditioning
"... Plog is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of Plog have been discussed. In this paper we further elaborate on the KR prowess of Plog by showing that: (i) ..."
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Plog is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of Plog have been discussed. In this paper we further elaborate on the KR prowess of Plog by showing that: (i) it can be used for causal and counterfactual reasoning and (ii) it provides an elaboration tolerant way for nonnaive conditioning. 1
DOI 10.1007/s1099400658331 Markov logic networks
, 2006
"... Abstract We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in ..."
Abstract
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Abstract We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a firstorder formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudolikelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a realworld database and knowledge base in a university domain illustrate the promise of this approach.
3Synthesis of Tiled Patterns Using Factor Graphs
"... Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to specify valid tile arrangements and to emphasiz ..."
Abstract
 Add to MetaCart
Patterns with pleasing structure are common in art, video games, and virtual worlds. We describe a method for synthesizing new patterns of tiles on a regular grid that are similar in appearance to a set of example patterns. Exemplars are used both to specify valid tile arrangements and to emphasize multitile structures. We model a pattern as a probabilistic graphical model called a factor graph. Factors represent the hard logical constraints between tiles, the soft statistical relationships that determine style, and the local dependencies between tiles at neighboring sites. We describe a simple method for learning factor functions from a small exemplar. We then synthesize new patterns through a stochastic search method that is inspired by MCSAT. Efficient synthesis is challenging because of the combination of hard and soft constraints. Our synthesis algorithm, called BLOCKSS, scales linearly with the number of tiles and the hardness of the problem. We use our technique to model building facades, cities, and decorative patterns.