  Many systems are naturally modeled as Markov Decision Processes (MDPs),
combining probabilities and strategic actions. Given a model of a system as an
MDP and some logical specification of system behavior, the goal of synthesis is
to find a policy that maximizes the probability of achieving this behavior. A
popular choice for defining behaviors is Linear Temporal Logic (LTL). Policy
synthesis on MDPs for properties specified in LTL has been well studied. LTL,
however, is defined over infinite traces, while many properties of interest are
inherently finite. Linear Temporal Logic over finite traces (LTLf) has been
used to express such properties, but no tools exist to solve policy synthesis
for MDP behaviors given finite-trace properties. We present two algorithms for
solving this synthesis problem: the first via reduction of LTLf to LTL and the
second using native tools for LTLf. We compare the scalability of these two
approaches for synthesis and show that the native approach offers better
scalability compared to existing automaton generation tools for LTL.
