Game-theoretic algorithms are commonly benchmarked on recreational games, classical constructs from economic theory such as congestion and dispersion games, or entirely random game instances. While the past two decades have seen the rise of security games - grounded in real-world scenarios like patrolling and infrastructure protection - their practical evaluation has been hindered by limited access to the datasets used to generate them. In particular, although the structural components of these games (e.g., patrol paths derived from maps) can be replicated, the critical data defining target values - central to utility modeling - remain inaccessible. In this paper, we introduce a flexible framework that leverages open-access datasets to generate realistic matrix and security game instances. These include animal movement data for modeling anti-poaching scenarios and demographic and infrastructure data for infrastructure protection. Our framework allows users to customize utility functions and game parameters, while also offering a suite of preconfigured instances. We provide theoretical results highlighting the degeneracy and limitations of benchmarking on random games, and empirically compare our generated games against random baselines across a variety of standard algorithms for computing Nash and Stackelberg equilibria, including linear programming, incremental strategy generation, and self-play with no-regret learners.

Neural Information Processing Systems http://nips.cc/

In this work, we initiate a theoretical study of the problem above.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Motivated by the practical problem of designing a security deployment strategy to protect targets from an adversary the author(s) model and study this as a Stackelberg game. The main result of the author(s) is that the defender can efficiently learn the payoffs of the adversary by carefully deploying resources and observing the adversary's attacks. Clearly, this setting may not be viable in the cases where the cost incurred by the defender on a successful attack is large (such as a terrorist attack) but perhaps is a reasonable strategy for other cases such as drug smuggling. The main result of the paper is a probably approximately optimal algorithm that finds a defender optimal strategy by learning from polynomial (in the number of targets and encoding length of the problem) number of attacks from the adversary.

Neural Information Processing Systems http://nips.cc/

One primary objective of game theory is to predict the behaviors of agents through equilibrium concepts in a given game. In practice, however, we may observe some equilibrium behaviors of agents, but the game itself turns out to be unknown.

To counter an imminent multi-drone attack on a city, defenders have deployed drones across the city. These drones must intercept/eliminate the threat, thus reducing potential damage from the attack. We model this as a Sequential Stackelberg Security Game, where the defender first commits to a mixed sequential defense strategy, and the attacker then best responds. We develop an efficient algorithm called S2D2, which outputs a defense strategy. We demonstrate the efficacy of S2D2 in extensive experiments on data from 80 real cities, improving the performance of the defender in comparison to greedy heuristics based on prior works. We prove that under some reasonable assumptions about the city structure, S2D2 outputs an approximate Strong Stackelberg Equilibrium (SSE) with a convenient structure. Introduction There has been a lot of recent concern about multi-drone attacks [1, 2, 3, 4, 5, 6, 7, 8], especially in highly populated urban areas where not all countermeasures can be ...