Definition
In artificial intelligence and probability theory, Markov processes are fundamental models used to describe systems that transition between states randomly. The core principle is the Markov property, which asserts that the probability of moving to a future state is conditioned solely on the present state, ignoring the history of how the system arrived there. This simplification allows for efficient computation in complex dynamic environments. Markov Decision Processes (MDPs) extend this concept to include actions and rewards, forming the backbone of many reinforcement learning algorithms.
Summary
Markov refers to stochastic processes where the future state depends only on the current state, adhering to the Markov property of memorylessness.
Key Concepts
- Markov Property
- Memorylessness
- State Transition
- Stochastic Process
Use Cases
- Reinforcement learning agents
- Natural language generation
- Predictive maintenance systems