A Partially Observable Markov Decision Process (POMDP) is a framework used in decision-making where the agent does not have complete information about the state of the environment. In a POMDP, the agent makes decisions based on a set of possible states, actions, and rewards, while receiving only partial observations of the true state. This uncertainty complicates the decision-making process, as the agent must infer the hidden state from the observations. POMDPs extend the concept of Markov Decision Processes (MDPs) by incorporating the idea of partial observability. They are widely used in various fields, including robotics, artificial intelligence, and economics, to model situations where an agent must act optimally despite incomplete information about its surroundings.