Logical Puzzle

Logical puzzles, encompassing diverse problem types like Sudoku, Zebra puzzles, and Knights and Knaves, serve as a benchmark for evaluating artificial intelligence's reasoning capabilities. Current research focuses on applying and adapting large language models (LLMs), reinforcement learning (RL) algorithms, and constraint satisfaction techniques (e.g., using SAT solvers and ZDDs) to solve these puzzles, often incorporating methods like chain-of-thought prompting or multi-agent systems to improve performance. These studies contribute to a deeper understanding of how AI systems reason and learn, informing the development of more robust and efficient problem-solving algorithms with implications for broader applications in natural language understanding and automated reasoning.