Foundations of Artificial Intelligence: Search, Logic, and Exam Prep

Mastering the core mathematical and logical pillars of artificial intelligence is essential for academic success and competitive exam preparation. This text-based course guides you through the foundational theories of AI, breaking down complex algorithmic structures into clear, readable explanations. By completing this material, you will develop a strong theoretical and practical grasp of classical AI, enabling you to solve complex problems in search optimization, formal logic, and reasoning under uncertainty. What you'll learn: Understand fundamental state-space search strategies, including heuristic search and adversarial search algorithms; Apply propositional and first-order logic to represent knowledge and perform formal reasoning; Analyze reasoning under uncertainty using probability theory, Bayes' theorem, and belief networks; Practice solving typical exam-style problems with clear, step-by-step written explanations; Explore modern agent-based architectures and how classical search scales to contemporary AI systems. The course begins with foundational definitions of intelligent agents before moving systematically through search algorithms, formal logical deduction, and probabilistic models. You will progress from basic terminology to solving advanced analytical problems entirely through written text and structured examples. This course is designed for students preparing for competitive computer science exams, academic assessments, or anyone seeking a rigorous, mathematically-sound introduction to classical AI with no advanced prerequisites. Start reading today to master the core principles of artificial intelligence and build a solid academic foundation.