  We present a novel way of performing kinetic Monte Carlo simulations which
does not require an {\it a priori} list of diffusion processes and their
associated energetics and reaction rates.
  Rather, at any time during the simulation, energetics for all possible
(single or multi-atom) processes, within a specific interaction range, are
either computed accurately using a saddle point search procedure, or retrieved
from a database in which previously encountered processes are stored. This
self-learning procedure enhances the speed of the simulations along with a
substantial gain in reliability because of the inclusion of many-particle
processes.
  Accompanying results from the application of the method to the case of
two-dimensional Cu adatom-cluster diffusion and coalescence on Cu(111) with
detailed statistics of involved atomistic processes and contributing diffusion
coefficients attest to the suitability of the method for the purpose.
