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ECAI
2010
Springer
13 years 1 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
KI
2007
Springer
13 years 10 months ago
Extending Markov Logic to Model Probability Distributions in Relational Domains
Abstract. Markov logic, as a highly expressive representation formalism that essentially combines the semantics of probabilistic graphical models with the full power of first-orde...
Dominik Jain, Bernhard Kirchlechner, Michael Beetz
ICMLA
2009
13 years 2 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
AAAI
2012
11 years 7 months ago
Goal Recognition with Markov Logic Networks for Player-Adaptive Games
Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital ...
Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, Jam...
AAAI
2010
13 years 6 months ago
Structure Learning for Markov Logic Networks with Many Descriptive Attributes
Many machine learning applications that involve relational databases incorporate first-order logic and probability. Markov Logic Networks (MLNs) are a prominent statistical relati...
Hassan Khosravi, Oliver Schulte, Tong Man, Xiaoyua...