Satisfiability Modulo Theory

Satisfiability Modulo Theories (SMT) extends the Boolean satisfiability problem by incorporating constraints from various mathematical theories, enabling the solving of complex problems involving both logical and numerical reasoning. Current research focuses on applying SMT solvers to diverse areas, including multi-agent pathfinding, dynamic task allocation in robotics, and verification of deep neural networks, often leveraging incremental solving techniques and novel encodings to improve efficiency. This powerful framework has significant implications for advancing artificial intelligence, particularly in areas requiring both symbolic reasoning and numerical computation, as well as for improving the reliability and safety of complex systems.