abstract = "proposed is a general system to infer symbolic policy
functions for distributed reactive scheduling in
non-stationary environments. The job shop problem is
only used as a validating case study. Our system is
based both on an original distributed scheduling model
and on genetic programming for the inference of
symbolic policy functions. The purpose is to determine
heuristic policies that are local in time, long term
near-optimal, and robust with respect to perturbations.
Furthermore, the policies are local in state space: the
global decision problem is split into as many decision
problems as there are agents, i.e. machines in the job
shop problem. If desired, the genetic algorithm can use
expert knowledge as a priori knowledge, via
implementation of the symbolic representation of the
policy functions.",
notes = "{"}To be published in the proceedings of the Seventh
Annual Florida Artificial Intelligence Research
Symposium{"} DGT/DEA/IA2 December 1993
Combination of GP and Giffler and Thompson algorithm",