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Markov Logic Networks

by Matthew Richardson, Pedro Domingos - MACHINE LEARNING , 2006
"... We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
Abstract - Cited by 811 (39 self) - Add to MetaCart
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects

Hybrid Markov Logic Networks

by Jue Wang, Pedro Domingos
"... Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain continuo ..."
Abstract - Cited by 42 (1 self) - Add to MetaCart
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain

Learning the structure of Markov logic networks

by Stanley Kok, Pedro Domingos - In Proceedings of the 22nd International Conference on Machine Learning , 2005
"... Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive l ..."
Abstract - Cited by 114 (20 self) - Add to MetaCart
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this paper we develop an algorithm for learning the structure of MLNs from relational databases, combining ideas from inductive

Discriminative training of markov logic networks

by Parag Singla, Pedro Domingos - In Proc. of the Natl. Conf. on Artificial Intelligence , 2005
"... Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be lear ..."
Abstract - Cited by 108 (19 self) - Add to MetaCart
Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can

Reinforcement learning with markov logic networks

by Weiwei Wang, Yang Gao, Xingguo Chen, Shen Ge - In Proceedigns of European Workshop on Reinforcement Learning , 2008
"... Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines first-order logic and graphical model and it can represent a ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Abstract. In this paper, we propose a method to combine reinforcement learning (RL) and Markov logic networks (MLN). RL usually does not consider the inherent relations or logical connections of the features. Markov logic networks combines first-order logic and graphical model and it can represent

Transfer learning with Markov logic networks

by Lilyana Mihalkova, Raymond Mooney - 2006. Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning
"... We propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our ap-proach is that it first diagnoses the provided source MLN and then focuses on re-learning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate that t ..."
Abstract - Cited by 8 (2 self) - Add to MetaCart
We propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our ap-proach is that it first diagnoses the provided source MLN and then focuses on re-learning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate

On Generative Parameterisations of Markov logic networks

by James Cussens, P (x Z
"... Given some fixed first-order language, a Markov logic network (MLN) (Domingos et al., 2008) uses weighted formulae to define a probability distribution over Herbrand interpretations of the language. (To save space a Herbrand interpretation will be called a ‘world’ from now on. In this paper all such ..."
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Given some fixed first-order language, a Markov logic network (MLN) (Domingos et al., 2008) uses weighted formulae to define a probability distribution over Herbrand interpretations of the language. (To save space a Herbrand interpretation will be called a ‘world’ from now on. In this paper all

Discriminative Training of Markov Logic Networks

by unknown authors
"... Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can be lear ..."
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Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs) accomplish this by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Model parameters (i.e., clause weights) can

Discriminative Learning with Markov Logic Networks

by Tuyen N. Huynh
"... Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted clauses, are a simple but powerful SRL formalism that combines the expressivity of first-order logic with ..."
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Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted clauses, are a simple but powerful SRL formalism that combines the expressivity of first-order logic

Event modeling and recognition using markov logic networks

by Son D. Tran, Larry S. Davis - IN ECCV , 2008
"... We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as first-order logic production rules with associated weights to indicate their con ..."
Abstract - Cited by 83 (4 self) - Add to MetaCart
their confidence. These rules are used in combination with a relaxed deduction algorithm to construct a network of grounded atoms, the Markov Logic Network. The network is used to perform probabilistic inference for input queries about events of interest. The system’s performance is demonstrated on a number
Results 1 - 10 of 762,581