Abstract:

To solve the uncertainty and randomization problems which happen in virtual maintenance process, a novel operation strategy planning model based on Q-learning algorithm is presented. The virtu- al maintenance process is transformed into a state transition process by using various actions. Correcting mechanism and inverse solution are used to solve task planning problem. To guarantee revolting continu- ously evolving feasible strategy, the characteristics matching mechanism and sequence constraint mecha- nism are proposed to aid in finding the optimal strategy. In the evolution process, a genetic algorithm is used to adjust the probability distribution of action value to avoid reinforcing early action with high Q-val- ue. Finally, an operation strategy optimal example for the virtual maintenance system is given to show that right action always can receive high Q-value in the evolution, which illustrates the feasibility and ap- plicability of the proposed methodology.

Key words: artificial intelligence, virtual maintenance, Q-learning, genetic algorithm, operation plan- ning