  POMDPs are standard models for probabilistic planning problems, where an
agent interacts with an uncertain environment. We study the problem of
almost-sure reachability, where given a set of target states, the question is
to decide whether there is a policy to ensure that the target set is reached
with probability 1 (almost-surely). While in general the problem is
EXPTIME-complete, in many practical cases policies with a small amount of
memory suffice. Moreover, the existing solution to the problem is explicit,
which first requires to construct explicitly an exponential reduction to a
belief-support MDP. In this work, we first study the existence of
observation-stationary strategies, which is NP-complete, and then small-memory
strategies. We present a symbolic algorithm by an efficient encoding to SAT and
using a SAT solver for the problem. We report experimental results
demonstrating the scalability of our symbolic (SAT-based) approach.
