# Catcher-Evader Games

arXiv: https://arxiv.org/abs/1602.01896 · HTML: https://mlsanity.com/paper/1602.01896/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2016.

## Built on by (most cited first, 3 of 3)

- [Regret Minimization in Stackelberg Games with Side Information](https://mlsanity.com/paper/2402.08576/) (2024)
- [Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information](https://mlsanity.com/paper/2502.00204/) (2025)
- [A Survey of Decision Making in Adversarial Games](https://mlsanity.com/paper/2207.07971/) (2022)

## Abstract (arXiv)

Algorithms for computing game-theoretic solutions have recently been applied to a number of security domains. However, many of the techniques developed for compact representations of security games do not extend to em Bayesian security games, which allow us to model uncertainty about the attacker's type. In this paper, we introduce a general framework of em catcher-evader games that can capture Bayesian security games as well as other game families of interest. We show that computing Stackelberg strategies is NP-hard, but give an algorithm for computing a Nash equilibrium that performs well in experiments. We also prove that the Nash equilibria of these games satisfy the em interchangeability property, so that equilibrium selection is not an issue.