Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels that the user enjoys. In this paper we present a path prediction algorithm that exploits the machine learning algorithm of learning automata. The decision of the learning automaton is driver by the movement patterns of a single user but is also affected by the aggregated patterns demonstrated by all users. Simulations of the algorithm, performed using a Realistic Mobility Pattern Generator, show increased prediction accuracy.