Learning Models of Relational Stochastic Processes

Processes involving change over time, uncertainty, and rich relational structure are common in the real world, but no general algorithms exist for learning models of them. In this paper we show how Markov logic networks (MLNs), a recently developed approach to combining logic and probability, can be applied to time-changing domains. We then show how existing algorithms for parameter and structure learning in MLNs can be extended to this setting. We apply this approach in two domains: modeling the spread of research topics in scientific communities, and modeling faults in factory assembly processes. Our experiments show that it greatly outperforms purely logical (ILP) and purely probabilistic (DBN) learners.