A Partially Observable Markov Decision Process (Pomdp)
A mathematical framework used to model sequential decision-making processes under uncertainty, where the agent cannot directly observe the underlying state of the system. Instead, it must maintain a sensor model, which is the probability distribution of different observations given the current state.
Areas of application
- Robotics
- Autonomous vehicles
- Medical decision-making
- Financial modeling
- Security systems
Example
Consider an autonomous vehicle navigating through a dense fog. The vehicle’s sensors can only detect the distance and velocity of nearby objects, but not their exact location. Using POMDP, we can model the uncertainty in the vehicle’s perception of the environment and make decisions based on the sensor observations.