A Markov Decision Process (MDP) is a formal framework used in artificial intelligence and operations research to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. An MDP is defined by a set of states, a set of actions available in each state, transition probabilities that describe the likelihood of moving between states after taking actions, and a reward function that assigns values to state transitions. MDPs are crucial in reinforcement learning, as they provide the foundation for algorithms that seek to learn optimal policies that maximize cumulative rewards over time. Applications of MDPs include robotics, game AI, and various optimization problems, making them a fundamental concept in the study of artificial intelligence.