  Recently, Dallal, Neider, and Tabuada studied a generalization of the
classical game-theoretic model used in program synthesis, which additionally
accounts for unmodeled intermittent disturbances. In this extended framework,
one is interested in computing optimally resilient strategies, i.e., strategies
that are resilient against as many disturbances as possible. Dallal, Neider,
and Tabuada showed how to compute such strategies for safety specifications. In
this work, we compute optimally resilient strategies for a much wider range of
winning conditions and show that they do not require more memory than winning
strategies in the classical model. Our algorithms only have a polynomial
overhead in comparison to the ones computing winning strategies. In particular,
for parity conditions, optimally resilient strategies are positional and can be
computed in quasipolynomial time.
