12.00 - 1.00pm
Probabilistic Tabled Logic Programming with Application to Model Checking
Stonybrook University~ NY, U.S.A.
There has been extensive work on using logical inference, especially tabled resolution, for program analysis and model checking. These works primarily cast the verification problems in terms of query evaluation over logic programs, and use the underlying inference procedures to derive efficient implementations directly from the logic programs. Recent work has extended these results to probabilistic systems by leveraging work on probabilistic logic programming. In this talk we will review the work on combining logical and statistical inference in probabilistic logic programming. We will cover approximate inference and parameter learning problems in this setting as well. We will then describe how a number of problems in probabilistic model checking and probabilistic program analysis can effectively implemented in terms of query evaluation.