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 dem...

• Type: Journal
• Year: 2010
• Where: ECAI
• Authors: Dominik Jain, Andreas Barthels, Michael Beetz
• Keywords: Artificial Intelligence, ECAI 2010, Markov Logic, Parameters, Relational Domains