Dynamic Programming (DP) is an algorithmic technique in AI used to solve complex problems by breaking them into smaller, overlapping subproblems and solving each subproblem only once. In AI, it helps in decision-making tasks like reinforcement learning, natural language processing (NLP), computer vision, robotics, and game AI. For example, in reinforcement learning, DP methods like Value Iteration and Policy Iteration optimize policies for agents. In NLP, algorithms like Viterbi and Dynamic Time Warping use DP to find optimal sequences. DP enables efficient solutions in AI by reducing redundant computations and storing intermediate results. It’s fundamental in pathfinding, sequence modeling, and optimization problems, making it a key tool in building AI systems. Mastering DP is essential for AI because it transforms exponential-time problems into polynomial-time solutions, enabling AI to handle large, complex tasks effectively.