Algorithmic Game Theory: Solving Strategic Problems

Algorithmic puzzles based on game theory require structured thinking and mastery of state transitions. This course provides the foundational mathematical and computational tools necessary to tackle these strategic challenges. By the end of this course, you will be able to systematically analyze impartial games, identify winning and losing positions, and apply powerful techniques like recursion and dynamic programming to determine optimal moves. What you'll learn: * Understand the key concepts of impartial games, strategic moves, and terminal positions. * Apply recursive thinking and systematic state-space search to analyze game positions for optimal outcomes. * Master the systematic method of identifying winning (P) and losing (N) positions in finite games. * Practice modeling complex scenarios using graph theory and algebraic notation. * Learn to use memoization and basic dynamic programming techniques to optimize game analysis efficiency. The course begins with core terminology and definitions, gradually introducing techniques for analyzing simple games before moving on to complex, multi-step strategic problems and their efficient algorithmic solutions. This course is designed for absolute beginners interested in computer science, competitive programming, or mathematical problem-solving. No prior knowledge of advanced mathematics or game theory is required. Start building your strategic problem-solving skills today.