Paper Info
Title
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 belonging to particular classes. Models are intended to be independent of the set of objects to which these principles can be applied, and it is assumed that the principles will soundly generalize across arbitrary sets of objects. In this paper, we point out limitations of models that seek to represent the corresponding principles with a fixed set of parameters and discuss the conditions under which the soundness of fixed parameters is indeed questionable. We propose a novel representation formalism called adaptive Markov logic networks to allow more flexible representations of relational domains, which involve parameters that are dynamically adjusted to fit the properties of an instantiation by phrasing the model's parameters as functions over attributes of the instantiation at hand. We empirically demonstrate the value of our learning and representation system on a simple but well-motivated example domain.
Year
DOI
Venue
2010
10.3233/978-1-60750-606-5-937
ECAI
Keywords
Field
DocType
statistical relational model,novel representation formalism,representation system,flexible representation,adaptive markov logic network,arbitrary set,fixed parameter,learning statistical relational models,dynamic parameters,fixed set,relational domain,markov logic network,adaptive markov logic networks,relational model
Relational calculus,Computer science,Statistical relational learning,Markov chain,Artificial intelligence,Soundness,Formalism (philosophy),Machine learning
Conference
Volume
ISSN
Citations 
215
0922-6389
10
PageRank 
References 
Authors
0.66
9
3
Name
Order
Citations
PageRank
Dominik Jain115710.30
Andreas Barthels211215.54
Michael Beetz33784284.03