Safe Search for Stackelberg Equilibria in Extensive-Form Games
Chun Kai Ling, Noam Brown
Abstract
Stackelberg equilibrium is a solution concept in two-player games where the leader has commitment rights over the follower. In recent years, it has become a cornerstone of many security applications, including airport patrolling and wildlife poaching prevention. Even though many of these settings are sequential in nature, existing techniques pre-compute the entire solution ahead of time. In this paper, we present a theoretically sound and empirically effective way to apply search, which leverages extra online computation to improve a solution, to the computation of Stackelberg equilibria in general-sum games. Instead of the leader attempting to solve the full game upfront, an approximate "blueprint" solution is first computed offline and is then improved online for the particular subgames encountered in actual play. We prove that our search technique is guaranteed to perform no worse than the pre-computed blueprint strategy, and empirically demonstrate that it enables approximately solving significantly larger games compared to purely offline methods. We also show that our search operation may be cast as a smaller Stackelberg problem, making our method complementary to existing algorithms based on strategy generation.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1caee82b-e18f-44e4-8f5d-7a16bb9ba7d1Cited by top-tier papers1
Ask how each one uses itRelated papers
- Double-Oracle Sampling Method for Stackelberg Equilibrium Approximation in General-Sum Extensive-Form GamesJan Karwowski, Jacek MandziukAAAI 2020 · 17 citations
- Regret Minimization in Stackelberg Games with Side InformationKeegan Harris, Zhiwei Steven Wu, Maria-Florina BalcanNeurIPS 2024 · 13 citations
- Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement LearningMatthias Gerstgrasser, David C. ParkesICML 2023 · 27 citations
- Subgame Solving in Adversarial Team GamesBrian Hu Zhang, Luca Carminati, Federico Cacciamani, Gabriele Farina et al.NeurIPS 2022 · 10 citations
- When Can the Defender Effectively Deceive Attackers in Security Games?Thanh Nguyen, Haifeng XuAAAI 2022 · 4 citations
