A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends.

Randomized first-mover strategies of Stackelberg games are used in several deployed applications to allocate limited resources for the protection of critical infrastructure. Stackelberg games model the fact that a strategic attacker can surveil and exploit the defender's strategy, and randomization guards against the worst effects by making the defender less predictable. In accordance with the standard game-theoretic model of Stackelberg games, past work has typically assumed that the attacker has perfect knowledge of the defender's randomized strategy and will react correspondingly. In light of the fact that surveillance is costly, risky, and delays an attack, this assumption is clearly simplistic: attackers will usually act on partial knowledge of the defender's strategies. The attacker's imperfect estimate could present opportunities and possibly also threats to a strategic defender. In this paper, we therefore begin a systematic study of security games with limited surveillance. We propose a natural model wherein an attacker forms or updates a belief based on observed actions, and chooses an optimal response. We investigate the model both theoretically and experimentally. In particular, we give mathematical programs to compute optimal attacker and defender strategies for a fixed observation duration, and show how to use them to estimate the attacker's observation durations. Our experimental results show that the defender can achieve significant improvement in expected utility by taking the attacker's limited surveillance into account, validating the motivation of our work.

Building upon previous security applications of computational game theory, this paper presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed the deployment of PROTECT in Boston a success, and efforts are underway to test it in the port of New York, with the potential for nationwide deployment. PROTECT is premised on an attacker-defender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior - to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT's QR model more robustly handles real-world uncertainties than a perfect rationality model. Finally, in evaluating PROTECT, this paper provides real-world data: (i) comparison of human-generated vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team's (human mock attackers) analysis.

In all of these problems, we have limited with researchers in other disciplines, be "on the ground" security resources which prevent full security coverage with domain experts, and examine real-world constraints at all times; instead, limited security resources must be deployed and challenges that cannot be abstracted away. Together as intelligently taking into account differences in priorities an international community of multiagent researchers, we of targets requiring security coverage, the responses of can accomplish more! the adversaries to the security posture and potential uncertainty over the types, capabilities, knowledge and priorities

In all of these problems, we have limited be done; yet these are large-scale interdisciplinary research security resources which prevent full security coverage challenges that call upon multiagent researchers to work at all times; instead, limited security resources must be deployed with researchers in other disciplines, be "on the ground" intelligently taking into account differences in priorities with domain experts, and examine real-world constraints of targets requiring security coverage, the responses of and challenges that cannot be abstracted away. Together as the adversaries to the security posture and potential uncertainty an international community of multiagent researchers, we over the types, capabilities, knowledge and priorities can accomplish more! of adversaries faced.

Stackelberg games have been used in several deployed applications of game theory to make recommendations for allocating limited resources for protecting critical infrastructure. The resource allocation strategies are randomized to prevent a strategic attacker from using surveillance to learn and exploit patterns in the allocation. An important limitation of previous work on security games is that it typically assumes that attackers have perfect surveillance capabilities, and can learn the exact strategy of the defender. We introduce a new model that explicitly models the process of an attacker observing a sequence of resource allocation decisions and updating his beliefs about the defender's strategy. For this model we present computational techniques for updating the attacker's beliefs and computing optimal strategies for both the attacker and defender, given a specific number of observations. We provide multiple formulations for computing the defender's optimal strategy, including non-convex programming and a convex approximation. We also present an approximate method for computing the optimal length of time for the attacker to observe the defender's strategy before attacking. Finally, we present experimental results comparing the efficiency and runtime of our methods.

Defender-Attacker Stackelberg games are the foundations of toolsdeployed for computing optimal patrolling strategies in adversarialdomains such as the United states Federal Air Marshals Service and the UnitedStates Coast Guard, among others.In Stackelberg game models of these systems the attacker knows only theprobability that each target is covered by the defender, but isoblivious to the detailed timing of the coverage schedule.In many real-world situations, however, the attacker can observe thecurrent location of the defender and can exploit this knowledge toreason about the defender's future moves.We study Stackelberg security games in which the defender sequentiallymoves between targets, with moves constrained by an exogenouslyspecified graph, while the attacker can observe the defender's currentlocation and his (stochastic) policy concerning future moves. We offerfive contributions: (1) We model this adversarial patrolling game (APG) as a stochastic game with special structure and presentseveral alternative formulations that leverage the general non-linearprogramming (NLP) approach for computing equilibria in zero-sumstochastic games. We show that our formulations yield significantlybetter solutions than previous approaches. (2) We extend theNLP formulation for APG allow for attacks that may take multiple timesteps to unfold.(3) We provide anapproximate MILP formulation that uses discrete defender moveprobabilities. (4) We experimentally demonstrate the efficacy of anNLP-based approach, and systematically study the impact of networktopology on the results.(5) We extend our model to allow the defender to construct the graph constraining his moves, at some cost, and offer novel algorithms for this setting, finding that a MILP approximation is much more effective than the exact NLP in this setting.

Given the real-world deployments of attacker-defender Stackelberg security games, robustness to deviations from expected attacker behaviors has now emerged as a critically important issue. This paper provides four key contributions in this context. First, it identifies a fundamentally problematic aspect of current algorithms for security games. It shows that there are many situations where these algorithms face multiple equilibria, and they arbitrarily select one that may hand the defender a significant disadvantage, particularly if the attacker deviates from its equilibrium strategies due to unknown constraints. Second, for important subclasses of security games, it identifies situations where we will face such multiple equilibria. Third, to address these problematic situations, it presents two equilibrium refinement algorithms that can optimize the defender's utility if the attacker deviates from equilibrium strategies. Finally, it experimentally illustrates that the refinement approach achieved significant robustness in consideration of attackers' deviation due to unknown constraints.

Stackelberg games have been widely used to model patrolling or monitoring problems in security. In a Stackelberg security game, the defender commits to a strategy and the adversary makes its decision with knowledge of the leader's commitment. Algorithms for computing the defender's optimal strategy are used in deployed decision-support tools in use by the Los Angeles International Airport (LAX), the Federal Air Marshals Service, and the Transportation Security Administration (TSA). Those algorithms take into account various resource usage constraints defined by human users. However, those constraints may lead to poor (even infeasible) solutions due to users' insufficient information and bounded rationality. A mixed-initiative approach, in which human users and software assistants (agents) collaborate to make security decisions, is needed. Efficient human-agent interaction process leads to models with higher overall solution quality. This paper preliminarily analyzes the needs and challenges for such a mixed-initiative approach.