Publications

In many multiagent domains, no single observation event is sufficient to determine that the behavior of individuals is suspicious. Instead, suspiciousness must be inferred from a combination of multiple events, where events refer to the individual’s interactions with other individuals. Hence, a detection system must employ a detector that combines evidence from multiple events, in contrast to most previous work, which focuses on the detection of a single, clearly suspicious event. This paper proposes a two-step detection system, where it first detects trigger events from multiagent interactions, and then combines the evidence to provide a degree of suspicion. The paper provides three key contributions: (i) proposes a novel detector that generalizes a utility-based plan recognition with arbitrary utility functions, (ii) specifies conditions that any reasonable detector should satisfy, and (iii) analyzes three detectors and compares them with the proposed approach. The results on a simulated airport domain and a dangerous-driver domain show that our new algorithm outperforms other approaches in several settings.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [8]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions.

The goal of this paper is to introduce a real-world challenge problem for researchers in multiagent systems and beyond, where our collective efforts may have a significant impact on activities in the real-world. The challenge is in applying game theory for security: Our goal is not only to introduce the problem, but also to provide exemplars of initial successes of deployed systems in this challenge problem arena, some key open research challenges and pointers to getting started in this research.

The goal of this paper is to introduce a real-world challenge problem for researchers in multiagent systems and beyond, where our collective efforts may have a significant impact on activities in the real-world. The challenge is in applying game theory for security: Our goal is not only to introduce the problem, but also to provide exemplars of initial successes of deployed systems in this challenge problem arena, some key open research challenges and pointers to getting started in this research.

Security is a critical concern around the world. In many security domains, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, avoiding schedule predictability, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types. Computational game theory can help design such unpredictable security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the emerging research area of “security games”; specifically, the research challenges posed by these applications include scaling up security games to largescale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges.

We study the problem of optimal resource allocation for packet selection and inspection to detect potential threats in large computer networks with multiple valuable computers of differing importance. An attacker tries to harm these targets by sending malicious packets from multiple entry points of the network; the defender thus needs to optimally allocate his resources to maximize the probability of malicious packet detection under network latency constraints. We formulate the problem as a graph-based security game with multiple resources of heterogeneous capabilities and propose a mathematical program for finding optimal solutions. Due to the very limited scalability caused by the large attacker’s strategy space and non-linearity of the program, we investigate solutions with approximated utility function and propose Grande, a novel polynomial approximate algorithm utilizing submodularity of the problem able to find solutions with a bounded error on problem of a realistic size.

Many strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks, and scale-free graphs. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected scale-free graphs.

Recently, game theory has been shown to be useful for reasoning about real-world security settings where security forces must protect critical assets from potential adversaries. In fact, there have been a number of deployed real-world applications of game theory for security (e.g., ARMOR at Los Angeles International Airport and IRIS for the Federal Air Marshals Service). Here, the objective is for the security force to utilize its limited resources to best defend their critical assets. An important factor in these real-world security settings is that the adversaries involved are humans who may not behave according to the standard assumptions of game-theoretic models. There are two key shortcomings of the approaches currently employed in these recent applications. First, human adversaries may not make the predicted rational decision. In such situations, where the security force has optimized against a perfectly rational opponent, a deviation by the human adversary can lead to adverse affects on the security force’s predicted outcome. Second, human adversaries are naturally creative and security domains are highly dynamic, making enumeration of all potential threats a practically impossible task and solving the resulting game, with current leading approaches, would be intractable. My thesis contributes to a very new area that combines algorithmic and experimental gametheory. Indeed, it examines a critical problem in applying game-theoretic techniques to situations where perfectly rational solvers must address human adversaries. In doing so it advances the study and reach of game theory to domains where software agents and humans may interact. More specifically, to address the first shortcoming, my thesis presents two separate algorithms to address potential deviations from the predicted rational decision by human adversaries. Experimental results, from a simulation that is motivated by a real-world security domain at Los Angeles International airport, demonstrated that both of my approaches outperform the currently deployed optimal algorithms which utilize standard game-theoretic assumptions and additional alternative algorithms against humans. In fact, one of my approaches is currently under evaluation in a real-world application to aid in resource allocation decisions for the United States Coast Guard. Towards addressing the second shortcoming of enumeration of a large number of potential adversary threat capabilities, I introduce a new game-theoretic model for efficiency, which additionally generalizes the previously accepted model for security domains. This new game-theoretic model for addressing human threat capabilities has seen real-world deployment and is under evaluation to aid the United States Transportation Security Administration in their resource allocation challenges.

Buildings are one of the major consumers of energy in the U.S. Both commercial and residential buildings account for about 42% of the national U.S. energy consumption. The majority of commercial buildings energy consumption is attributed to lighting (25%), space heating and cooling (25%), and ventilation (7%). Several research studies and industrial developments have focused on energy management based on maximum occupancy. However, fewer studies, with the objective of energy savings, have considered human preferences. This research focuses on office buildings’ occupants’ preferences and their contribution to the building energy conservation. Accordingly, occupants of selected university campus offices were asked to reduce lighting levels in their offices during work hours. Different types of information regarding their energy consumption were provided to the occupants. Email messages were used to communicate with the occupants. To monitor behavioral changes during the study, the test bed offices were equipped with wireless light sensors. The deployed light sensors were capable of detecting variations in light intensity, which was correlated with energy consumption. The impact of different types of information on occupant’s energy related behavior is presented.

In a Network Security Game (NSG), security agencies must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. A recent line of work relaxed the perfectrationality assumption of human adversary and showed significant advantages of incorporating the bounded rationality adversary models in non-networked security domains. Given that real-world NSG are often extremely complex and hence very difficult for humans to solve, it is critical that we address human bounded rationality when designing defender strategies. To that end, the key contributions of this paper include: (i) comprehensive experiments with human subjects using a web-based game that we designed to simulate NSGs; (ii) new behavioral models of human adversary in NSGs, which we train with the data collected from human experiments; (iii) new algorithms for computing the defender optimal strategy against the new models.

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficult to know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multiobjective optimization. Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier. The Pareto frontier can be generated by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives. Our contributions include: (i) an algorithm, Iterative -Constraints, for generating the sequence of CSOPs; (ii) an exact approach for solving an MILP formulation of a CSOP (which also applies to multi-objective optimization in more general Stackelberg games); (iii) heuristics that achieve speedup by exploiting the structure of security games to further constrain a CSOP; (iv) an approximate approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation and detailed experimental evaluation of the proposed approaches.

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficult to know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multiobjective optimization. Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier. The Pareto frontier can be generated by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives. Our contributions include: (i) an algorithm, Iterative -Constraints, for generating the sequence of CSOPs; (ii) an exact approach for solving an MILP formulation of a CSOP (which also applies to multi-objective optimization in more general Stackelberg games); (iii) heuristics that achieve speedup by exploiting the structure of security games to further constrain a CSOP; (iv) an approximate approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation and detailed experimental evaluation of the proposed approaches.

Illegal extraction of forest resources is fought, in many developing countries, by patrols that try to make this activity less profitable, using the threat of confiscation. With a limited budget, officials will try to distribute the patrols throughout the forest intelligently, in order to most effectively limit extraction. Prior work in forest economics has formalized this as a Stackelberg game, one very different in character from the discrete Stackelberg problem settings previously studied in the multiagent literature. Specifically, the leader wishes to minimize the distance by which a profit-maximizing extractor will trespass into the forest—or to maximize the radius of the remaining “pristine” forest area. The follower’s costbenefit analysis of potential trespass distances is affected by the likelihood of being caught and suffering confiscation. In this paper, we give a near-optimal patrol allocation algorithm and a 1/2-approximation algorithm, the latter of which is more efficient and yields simpler, more practical patrol allocations. Our simulations indicate that these algorithms substantially outperform existing heuristic allocations.

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [13]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions. In this paper, we perform a battery of experiments to explore the existence of agent-human emotional contagion. The first study is a between-subjects design, wherein subjects were shown an image of a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase in self-reported happiness between Neutral and Happy conditions with all characters tested and, to our knowledge, is the first ever study explicitly showing emotional contagion from a virtual agent to a human. We also examine the effects of participant gender, participant ethnicity, character attractiveness, and perceived character happiness and find that only perceived character happiness has a substantial impact on emotional contagion. In a second study, we examine the effect of a virtual character’s presence in a strategic situation by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic decision within the Stag Hunt and conducts the same experiment using a description of the same strategic situation with the decision already made. We find that the emotional impact returns again, particularly for women, implying that the contagion effect is substantially lessened in the presence of a strategic decision.

While three deployed applications of game theory for security have recently been reported, we as a community of agents and AI researchers remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towards that end, this paper presents PROTECT, a gametheoretic 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; PROTECT is currently being tested 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 for the first time 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.

While three deployed applications of game theory for security have recently been reported at AAMAS [12], we as a community remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towards that end, 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 for the first time 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.

Building upon previous security applications of computational game theory, this paper presents PROTECT, a gametheoretic 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 realworld data: (i) comparison of human-generated vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.

While three deployed applications of game theory for security have recently been reported at AAMAS [21], we as a community remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Towards that end, this chapter 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 does. Finally, in evaluating PROTECT, this chapter 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.

While game-theoretic approaches have been proposed for addressing complex security resource allocation problems, many of the standard game-theoretic assumptions fail to address human adversaries who security forces will likely face. To that end, approaches have been proposed that attempt to incorporate better models of human decision-making in these security settings. We take a new approach where instead of trying to create a model of human decisionmaking, we leverage ideas from robust optimization techniques. In addition, we extend our approach and the previous best performing approach to also address human anchoring biases under limited observation conditions. To evaluate our approach, we perform a comprehensive examination comparing the performance of our new approach against the current leading approaches to addressing human adversaries. Finally, in our experiments we take the first ever analysis of some demographic information and personality measures that may influence decision making in security games.

Game-theoretic approaches have been proposed for addressing the complex problem of assigning limited security resources to protect a critical set of targets. However, many of the standard assumptions fail to address human adversaries who security forces will likely face. To address this challenge, previous research has attempted to integrate models of human decision-making into the game-theoretic algorithms for security settings. The current leading approach, based on experimental evaluation, is derived from a wellfounded solution concept known as quantal response and is known as BRQR. One critical difficulty with opponent modeling in general is that, in security domains, information about potential adversaries is often sparse or noisy and furthermore, the games themselves are highly complex and large in scale. Thus, we chose to examine a completely new approach to addressing human adversaries that avoids the complex task of modeling human decision-making. We leverage and modify robust optimization techniques to create a new type of optimization where the defender’s loss for a potential deviation by the attacker is bounded by the distance of that deviation from the expected-value-maximizing strategy. To demonstrate the advantages of our approach, we introduce a systematic way to generate meaningful reward structures and compare our approach with BRQR in the most comprehensive investigation to date involving 104 security settings where previous work has tested only up to 10 security settings. Our experimental analysis reveals our approach performing as well as or outperforming BRQR in over 90% of the security settings tested and we demonstrate significant runtime benefits. These results are in favor of utilizing an approach based on robust optimization in these complex domains to avoid the difficulties of opponent modeling.