Structure Learning of Markov Logic Networks through Iterated Local Search

Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of MNs. State-of-theart structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a ...

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Marenglen Biba, Stefano Ferilli, Floriana Esposito
• Keywords: Artificial Intelligence, ECAI 2008, First-order Logic, Local Optima, Markov Logic Networks