  This paper addresses the problem of approximating the set of all solutions
for Multi-objective Markov Decision Processes. We show that in the vast
majority of interesting cases, the number of solutions is exponential or even
infinite. In order to overcome this difficulty we propose to approximate the
set of all solutions by means of a limited precision approach based on White's
multi-objective value-iteration dynamic programming algorithm. We prove that
the number of calculated solutions is tractable and show experimentally that
the solutions obtained are a good approximation of the true Pareto front.
