Automated Reasoning: Solving Problems with SAT and SMT

Many complex engineering and computational challenges—such as scheduling, resource allocation, and software verification—are too intricate to solve with manual coding. Automated reasoning allows you to translate these hard constraints into logical formulas that computer programs can solve instantly. This course guides you through the foundational concepts of propositional logic and satisfiability, showing you how to leverage powerful modern solver technologies to automate decision-making. By building a solid theoretical and practical foundation, you will transition from understanding basic logical operators to formulating and solving high-level constraint problems. You will learn how automated reasoning engines think under the hood and how to write clean, efficient specifications for them. What you'll learn: - Understand the core principles of propositional logic, resolution, and satisfiability. - Explore how modern Conflict-Driven Clause Learning (CDCL) solvers scale to handle massive formulas. - Model real-world constraints such as scheduling, puzzle-solving, and geometric layout problems. - Apply SMT (Satisfiability Modulo Theories) solvers to handle arithmetic and linear inequalities. - Write Python scripts using modern constraint solver libraries to automate logical reasoning. - Analyze basic program correctness and verification properties using formal logic. The course begins with essential definitions and theoretical foundations before moving on to practical modeling techniques. You will read clear conceptual explanations, study structured code snippets, and work through written exercises designed to build your problem-solving skills step-by-step. This course is designed for beginner programmers, computer science students, and analytical thinkers who want to explore constraint programming. No prior experience with formal logic or advanced mathematics is required. Start your journey into automated problem solving today.