  Two-player games on graphs provide the mathematical foundation for the study
of reactive systems. In the quantitative framework, an objective assigns a
value to every play, and the goal of player 1 is to minimize the value of the
objective. In this framework, there are two relevant synthesis problems to
consider: the quantitative analysis problem is to compute the minimal (or
infimum) value that player 1 can assure, and the boolean analysis problem asks
whether player 1 can assure that the value of the objective is at most $\nu$
(for a given threshold $\nu$). Mean-payoff expression games are played on a
multidimensional weighted graph. An atomic mean-payoff expression objective is
the mean-payoff value (the long-run average weight) of a certain dimension, and
the class of mean-payoff expressions is the closure of atomic mean-payoff
expressions under the algebraic operations of $\MAX,\MIN$, numerical complement
and $\SUM$. In this work, we study for the first time the strategy synthesis
problems for games with robust quantitative objectives, namely, games with
mean-payoff expression objectives. While in general, optimal strategies for
these games require infinite-memory, in synthesis we are typically interested
in the construction of a finite-state system. Hence, we consider games in which
player 1 is restricted to finite-memory strategies, and our main contribution
is as follows. We prove that for mean-payoff expressions, the quantitative
analysis problem is computable, and the boolean analysis problem is
inter-reducible with Hilbert's tenth problem over rationals --- a fundamental
long-standing open problem in computer science and mathematics.
