AUTHOR=Vu Mai H. , Zehfroosh Ashkan , Strother-Garcia Kristina , Sebok Michael , Heinz Jeffrey , Tanner Herbert G. TITLE=Statistical Relational Learning With Unconventional String Models JOURNAL=Frontiers in Robotics and AI VOLUME=5 YEAR=2018 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2018.00076 DOI=10.3389/frobt.2018.00076 ISSN=2296-9144 ABSTRACT=

This paper shows how methods from statistical relational learning can be used to address problems in grammatical inference using model-theoretic representations of strings. These model-theoretic representations are the basis of representing formal languages logically. Conventional representations include a binary relation for order and unary relations describing mutually exclusive properties of each position in the string. This paper presents experiments on the learning of formal languages, and their stochastic counterparts, with unconventional models, which relax the mutual exclusivity condition. Unconventional models are motivated by domain-specific knowledge. Comparison of conventional and unconventional word models shows that in the domains of phonology and robotic planning and control, Markov Logic Networks With unconventional models achieve better performance and less runtime with smaller networks than Markov Logic Networks With conventional models.