Splitting an LPMLN Program

Bin Wang, Zhizheng Zhang, Hongxiang Xu, Jun Shen

Abstract: The technique called splitting sets has been proven useful in simplifying the investigation of Answer Set Programming (ASP). In this paper, we investigate the splitting set theorem for LP MLN that is a new extension of ASP created by combining the ideas of ASP and Markov Logic Networks (MLN). Firstly, we extend the notion of splitting sets to LP MLN programs and present the splitting set theorem for LP MLN . Then, the use of the theorem for simplifying several LP MLN inference tasks is illustrated. After that, we give two parallel approaches for solving LP MLN programs via using the theorem. The preliminary experimental results show that these approaches are alternative ways to promote an LP MLN solver.