Splitting an LPMLN Program

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 LPMLN 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 LPMLN programs and present the splitting set theorem for LPMLN. Then, the use of the theorem for simplifying several LPMLN inference tasks is illustrated. After that, we give two parallel approaches for solving LPMLN programs via using the theorem. The preliminary experimental results show that these approaches are alternative ways to promote an LPMLN solver.

Authors

• Bin Wang Qihoo 360 AI Research
• Zhizheng Zhang Microsoft Research Asia
• Hongxiang Xu
• Jun Shen School of Computing and Information Technology, University of Wollongong, Australia

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