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Recent deployments of Stackelberg security games (SSG) have led to two competing approaches to handle boundedly rational human adversaries: (1) integrating models of human (adversary) decision-making into the game-theoretic algorithms , and (2) applying robust optimization techniques that avoid adversary modeling. A recent algorithm (MATCH) based on the(More)
There has been recent interest in applying Stackelberg games to infrastructure security, in which a defender must protect targets from attack by an adaptive adversary. In real-world security settings the adversaries are humans and are thus boundedly rational. Most existing approaches for computing defender strategies against boundedly rational adversaries(More)
Recent research on Green Security Games (GSG), i.e., security games for the protection of wildlife, forest and fisheries, relies on the promise of an abundance of available data in these domains to learn adversary behavioral models and determine game payoffs. This research suggests that adversary behavior models (capturing bounded rationality) can be(More)
Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS(More)
Given the real-world applications of Stackelberg security games (SSGs), addressing uncertainties in these games is a major challenge. Unfortunately, we lack any unified computational framework for handling uncertainties in SSGs. Current state-of-the-art has provided only compartmentalized robust algorithms that handle uncertainty exclusively either in the(More)
Stackelberg security games (SSGs) have been deployed in a number of real-world domains. One key challenge in these applications is the assessment of attacker payoffs, which may not be perfectly known. Previous work has studied SSGs with uncertain payoffs modeled by interval uncertainty and provided maximin-based robust solutions. In contrast, in this work(More)
Wildlife poaching presents a serious extinction threat to many animal species. Agencies (" defenders ") focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous(More)
Interdicting the flow of illegal goods (such as drugs and ivory) is a major security concern for many countries. The massive scale of these networks, however, forces defenders to make judicious use of their limited resources. While existing solutions model this problem as a Network Security Game (NSG), they do not consider humans' bounded rationality.(More)
Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of(More)