Just count the satisfied groundings: Scalable local-search and sampling based inference in MLNs
The main computational bottleneck in various sampling based and local-search based inference algorithms for Markov logic networks (e.g., Gibbs sampling, MC-SAT, MaxWalksat, etc.) is computing the number of groundings of a first-order formula that are true given a truth assignment to all of its ground atoms. We reduce this problem to the problem of counting the number of solutions of a constraint satisfaction problem (CSP) and show that during their execution, both sampling based and local-search based algorithms repeatedly solve dynamic versions of this counting problem. Deriving from the vast amount of literature on CSPs and graphical models, we propose an exact junction-tree based algorithm for computing the number of solutions of the dynamic CSP, analyze its properties, and show how it can be used to improve the computational complexity of Gibbs sampling and MaxWalksat. Empirical tests on a variety of benchmarks clearly show that our new approach is several orders of magnitude more scalable than existing approaches.
Proceedings of the National Conference on Artificial Intelligence
Venugopal, D., Sarkhel, S., & Gogate, V. (2015). Just count the satisfied groundings: Scalable local-search and sampling based inference in MLNs. Proceedings of the National Conference on Artificial Intelligence, 5, 3606-3612. Retrieved from https://digitalcommons.memphis.edu/facpubs/2918