Sound and Efficient Inference with Probabilistic and Deterministic Dependencies

Reasoning with both probabilistic and deterministic dependencies is important for many real-world problems, and in particular for the emerging field of statistical relational learning. However, probabilistic inference methods like MCMC or belief propagation tend to give poor results when deterministic or near-deterministic dependencies are present, and logical ones like satisfiability testing are inapplicable to probabilistic ones. In this paper we propose MC-SAT, an inference algorithm that combines ideas from MCMC and satisfiability. MC-SAT is based on Markov logic, which defines Markov networks using weighted clauses in first-order logic. From the point of view of MCMC, MC-SAT is a slice sampler with an auxiliary variable per clause, and with a satisfiabilitybased method for sampling the original variables given the auxiliary ones. From the point of view of satisfiability, MCSAT wraps a procedure around the SampleSAT uniform sampler that enables it to sample from highly non-uniform...

• Type: Conference
• Year: 2006
• Where: AAAI
• Authors: Hoifung Poon, Pedro Domingos
• Keywords: AAAI 2006, Deterministic Dependencies, Intelligent Agents, MCMC, Probabilistic Inference Methods