DC-SSAT: A divide-and-conquer approach to solving stochastic satisfiability problems efficiently

We present DC-SSAT, a sound and complete divide-and-conquer algorithm for solving stochastic satisfiability (SSAT) problems that outperforms the best existing algorithm for solving such problems (ZANDER) by several orders of magnitude with respect to both time and space. DC-SSAT achieves this performance by dividing the SSAT problem into subproblems based on the structure of the original instance, caching the viable partial assignments (VPAs) generated by solving these subproblems, and using these VPAs to construct the solution to the original problem. DC-SSAT does not save redundant VPAs and each VPA saved is necessary to construct the solution. Furthermore, DC-SSAT builds a solution that is already human-comprehensible, allowing it to avoid the costly solution rebuilding phase in ZANDER. As a result, DC-SSAT is able to solve problems using, typically, 1-2 orders of magnitude less space than ZANDER, allowing DC-SSAT to solve problems ZANDER cannot solve due to space constraints. And, in spite of its more parsimonious use of space, DC-SSAT is typically 1-2 orders of magnitude faster than ZANDER. We describe the DC-SSAT algorithm and present empirical results comparing its performance to that of ZANDER on a set of SSAT problems. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.