We consider a class of infinite-state stochastic games generated by stateless
pushdown automata (or, equivalently, 1-exit recursive state machines), where
the winning objective is specified by a regular set of target configurations
and a qualitative probability constraint `>0' or `=1'. The goal of one player
is to maximize the probability of reaching the target set so that the
constraint is satisfied, while the other player aims at the opposite. We show
that the winner in such games can be determined in PTIME for the `>0'
constraint, and both in NP and coNP for the `=1' constraint.
We study the computational complexity of central analysis problems for
One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented,
countable-state MDPs. OC-MDPs are equivalent to a controlled extension of
(discrete-time) Quasi-Birth-Death processes (QBDs), a stochastic model studied
heavily in queueing theory and applied probability. They can thus be viewed as
a natural ``adversarial'' version of a classic stochastic model. Alternatively,
they can also be viewed as a natural probabilistic/controlled extension of
classic one-counter automata.