Partially Observable Markov Decision Processes
Partially Observable Markov Decision Processes (POMDPs) are a mathematical framework for modeling decision-making in environments where an agent cannot directly observe the underlying state. They extend Markov Decision Processes (MDPs) by incorporating uncertainty about the state, requiring the agent to maintain a belief state (a probability distribution over possible states) based on observations and actions. POMDPs are widely used in robotics, artificial intelligence, and operations research to handle problems with imperfect information.
Developers should learn POMDPs when building systems that require decision-making under uncertainty, such as autonomous robots navigating unknown environments, dialogue systems with ambiguous user inputs, or resource allocation in unpredictable scenarios. They are essential for applications where sensors provide noisy or incomplete data, enabling agents to plan optimal actions despite partial observability, which is common in real-world AI and reinforcement learning tasks.