The workshop addresses the use of SAT techniques in solving search problems arising in Artificial Intelligence. In addition to SAT, the workshop covers also its various extensions like SAT modulo Theories (SMT), optimization problems such as MAXSAT and minimal model-reasoning, model-counting, quantified extensions of SAT such as Quantified Boolean Formulas (QBF) and Stochastic SAT, as well as representation languages closely related to SAT such as propositional Answer Set Programs (ASP) in Nonmonotonic Reasoning.
The goals of the workshop are A) bringing together researchers who work in different areas of Artificial Intelligence and use the SAT problem and its generalizations as a representation and/or solution framework for specific problems such as Diagnosis, Planning and Model-Checking, to name a few, B) to promote the use of SAT based techniques in Artificial Intelligence, and C) to identify important problems in SAT algorithms motivated by the application problems.
Topics of interest include (but are not limited to):