# Synthesizing Optimally Resilient Controllers

arXiv: https://arxiv.org/abs/1709.04854 · HTML: https://mlsanity.com/paper/1709.04854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Predicting Winning Regions in Parity Games via Graph Neural Networks (Extended Abstract)](https://mlsanity.com/paper/2210.09924/) (2022)
- [Resilient Strategies for Stochastic Systems: How Much Does It Take to Break a Winning Strategy?](https://mlsanity.com/paper/2602.24191/) (2026)

## Abstract (arXiv)

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.