The Rise of Approximate Model Counting: A Child of SAT Revolution
Title: The Rise of Approximate Model Counting: A Child of SAT Revolution
Abstract: The paradigmatic NP-complete problem of Boolean satisfiability (SAT) is a central problem in Computer Science. The past 20 years have witnessed a SAT revolution with the development of conflict-driven clause-learning (CDCL) SAT solvers. Such solvers combine a classical backtracking search with a rich set of effective heuristics. While 20 years ago SAT solvers were able to solve instances with at most a few hundred variables, modern SAT solvers solve instances with up to millions of variables in a reasonable time. The SAT revolution opens up opportunities to design practical algorithms with rigorous guarantees for problems in complexity classes beyond NP by replacing a NP oracle with a SAT Solver.
In this talk, we will discuss how we use SAT revolution to design practical algorithms for one of the fundamental problems in formal methods and artificial intelligence: model counting. Model counting has applications in diverse areas spanning neural network verification, reliability estimation, explainable AI, probabilistic inference, security vulnerability analysis, and the like. While model counting has been studied extensively by theoreticians for the past three decades. Yet, in spite of this extensive study, it has been extremely difficult to reduce this theory to practice. We examine how the process of revisiting and refining the theory to leverage the SAT revolution has led to the development of the the first scalable framework for model counting: ApproxMC. ApproxMC can handle industrial-scale problem instances involving hundreds of thousands of variables, while providing provably strong approximation guarantees.
Kuldeep Meel is the Sung Kah Kay Assistant Professor in the Computer Science Department of School of Computing at the National University of Singapore where he also holds President's Young Professorship. His research interests lie at the intersection of Formal Methods and Artificial Intelligence. He is a recipient of the 2019 NRF Fellowship for AI and was named AI’s 10 to Watch by IEEE Intelligent Systems in 2020. His work received the 2018 Ralph Budd Award for Best PhD Thesis in Engineering, 2014 Outstanding Masters Thesis Award from Vienna Center of Logic and Algorithms and Best Student Paper Award at CP 2015. He received his Ph.D. (2017) and M.S. (2014) degree from Rice University, and B. Tech. (with Honors) degree (2012) in Computer Science and Engineering from Indian Institute of Technology, Bombay.