The Markov property is the dependence of a system's future state probability distribution solely on the present state, excluding any dependence on past system history.
The presence of the Markov property saves computing resource requirements in terms of memory and processing in AI implementations, since no indexing, retrieval, or calculations involving past states is required.
However, the Markov property is often an unrealistic and too strong assumption.
Precisely, what limitations does the Markov property place on real-time learning?
