  We study best-effort strategies (aka plans) in fully observable
nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on
Finite Traces (LTLf). The notion of best-effort strategy has been introduced to
also deal with the scenario when no agent strategy exists that fulfills the
goal against every possible nondeterministic environment reaction. Such
strategies fulfill the goal if possible, and do their best to do so otherwise.
We present a game-theoretic technique for synthesizing best-effort strategies
that exploit the specificity of nondeterministic planning domains. We formally
show its correctness and demonstrate its effectiveness experimentally,
exhibiting a much greater scalability with respect to a direct best-effort
synthesis approach based on re-expressing the planning domain as generic
environment specifications.
