Fragility and Robustness in Mean-Payoff Adversarial Stackelberg Games

TL;DR

This paper analyzes the robustness of strategies in mean-payoff Stackelberg games, revealing that while zero-sum cases are robust, nonzero-sum cases are fragile, and proposes solutions for robust strategy synthesis.

Contribution

It introduces a new solution concept for robust strategies in nonzero-sum mean-payoff Stackelberg games against modeling errors and sub-optimal responses.

Findings

Robustness guaranteed in zero-sum games.

Optimal strategies are fragile in nonzero-sum games.

Proposes a new approach for robust strategy synthesis.

Abstract

Two-player mean-payoff Stackelberg games are nonzero-sum infinite duration games played on a bi-weighted graph by Leader (Player 0) and Follower (Player 1). Such games are played sequentially: first, Leader announces her strategy, second, Follower chooses his best-response. If we cannot impose which best-response is chosen by Follower, we say that Follower, though strategic, is adversarial towards Leader. The maximal value that Leader can get in this nonzero-sum game is called the adversarial Stackelberg value (ASV) of the game. We study the robustness of strategies for Leader in these games against two types of deviations: (i) Modeling imprecision - the weights on the edges of the game arena may not be exactly correct, they may be delta-away from the right one. (ii) Sub-optimal response - Follower may play epsilon-optimal best-responses instead of perfect best-responses. First, we show that if the game is zero-sum then robustness is guaranteed while in the nonzero-sum case, optimal strategies for ASV are fragile. Second, we provide a solution concept to obtain strategies for Leader that are robust to both modeling imprecision, and as well as to the epsilon-optimal responses of Follower, and study several properties and algorithmic problems related to this solution concept.