Composing approximated algorithms based on Hopfield neural network for building a resource-bounded scheduler

In previous work (J.-M. Gallone and F. Charpillet, 1996), we have studied the Hopfield artificial neural network model and its use for solving a particular scheduling problem: non preemptive tasks with release times, deadlines and computation times to be scheduled on several uniform machines. We presented an iterative approach based on Hopfield networks which enables resource bounded reasoning. We have validated our approach on a great number of randomly generated examples. Results are better than an efficient scheduling heuristics when no timing constraint exists and our system is able to adapt its behavior when timing constraints are imposed by the application. We extend this work by studying the incidence of two kinds of approximations on the processing time and on the success rate, so as to decide what sequence of activations for the contract will be likely to give the best success rate.