Publications

Strong Stackelberg equilibrium (SSE) is the standard solution concept of Stackelberg security games. The SSE assumes that the follower breaks ties in favor of the leader and this is widely acknowledged and justified by the assertion that the defender can often induce the attacker to choose a preferred action by making an infinitesimal adjustment to her strategy. Unfortunately, in security games with resource assignment constraints, the assertion might not be valid. To overcome this issue, inspired by the notion of inducibility and the pessimistic Stackelberg equilibrium [20, 21], this paper presents the inducible Stackelberg equilibrium (ISE), which is guaranteed to exist and avoids overoptimism as the outcome can always be induced with infinitesimal strategy deviation. Experimental evaluation unveils the significant overoptimism and sub-optimality of SSE and thus, verifies the advantage of the ISE as an alternative solution concept.

To improve cyber defense, researchers have developed algorithms to allocate limited defense resources optimally. Through signaling theory, we have learned that it is possible to trick the human mind when using deceptive signals. The present work is an initial step towards developing a psychological theory of cyber deception. We use simulations to investigate how humans might make decisions under various conditions of deceptive signals in cyber-attack scenarios. We created an Instance-Based Learning (IBL) model of the attacker decisions using the ACT-R cognitive architecture. We ran simulations against the optimal deceptive signaling algorithm and against four alternative deceptive signal schemes. Our results show that the optimal deceptive algorithm is more effective at reducing the probability of attack and protecting assets compared to other signaling conditions, but it is not perfect. These results shed some light on the expected effectiveness of deceptive signals for defense. The implications of these findings are discussed.

Youth homelessness has reached a concerning level of prevalence in the United States. Many communities have attempted to address this problem by creating coordinated community responses, typically referred to as Coordinated Entry Systems (CES). In such systems, agencies within a community pool their housing resources in a centralized system. Youth seeking housing are first assessed for eligibility and vulnerability and then linked to appropriate housing resources. The most widely adopted tool for assessing youth vulnerability is the Transition Age Youth-Vulnerability Index-Service Prioritization Decision Assistance Tool (TAY-VI-SPDAT): Next Step Tool (NST) for homeless youth. To date, no evidence has been amassed to support the value of using this tool or its proposed scoring schematic to prioritize housing resources. Similarly, there is little evidence on the outcomes of youth whose placements are determined by the tool. This article presents the first comprehensive and rigorous evaluation of the connection between vulnerability scores, housing placements, and stability of housing outcomes using data from the Homeless Management Information System (HMIS) collected between 2015 and 2017 from 16 communities across the United States. The two primary aims are (1) to investigate the degree to which communities are using the tool’s recommendations when placing youth into housing programs, and (2) to examine how effectively NST scores distinguish between youth in greater need of formal housing interventions from youth who may be able to self-resolve or return to family successfully. High vulnerability scores at intake were associated with higher odds of continued homelessness without housing intervention, suggesting the tool performs well in predicting youth that need to be prioritized for housing services in the context of limited resources. The majority of low scoring youth appear to return home or self-resolve and remain stably exited from homelessness. Youth placed in permanent supportive housing (PSH) had low recorded returns to homelessness, regardless of their NST score. Youth with vulnerability scores up to 10 who were placed in rapid rehousing (RRH) also had low returns to homelessness, but success was much more variable for higher-scoring youth.

The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm any animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we discuss SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time [Bondi et al., 2018b]. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design of SPOT, (ii) efficient processing techniques to ensure usability in the field, (iii) evaluation of SPOT based on historical videos and a real-world test run by the end-users, Air Shepherd, in the field, and (iv) the use of AirSim for live demonstration of SPOT. The promising results from a field test have led to a plan for larger-scale deployment in a national park in southern Africa. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.

More than 1 million homicides, robberies, and aggravated assaults occur in the United States each year. These crimes are often further classified into different types based on the circumstances surrounding the crime (e.g., domestic violence, gang-related). Despite recent technological advances in AI and machine learning, these additional classification tasks are still done manually by specially trained police officers. In this paper, we provide the first attempt to develop a more automatic system for classifying crimes. In particular, we study the question of classifying whether a given violent crime is gang-related. We introduce a novel Partially Generative Neural Networks (PGNN) that is able to accurately classify gang-related crimes both when full information is available and when there is only partial information. Our PGNN is the first generative-classification model that enables to work when some features of the test examples are missing. Using a crime event dataset from Los Angeles covering 2014-2016, we experimentally show that our PGNN outperforms all other typically used classifiers for the problem of classifying gangrelated violent crimes.

A wealth of algorithms centered around (integer) linear programming have been proposed to compute equilibrium strategies in security games with discrete states and actions. However, in practice many domains possess continuous state and action spaces. In this paper, we consider a continuous space security game model with infinite-size action sets for players and present a novel deep learning based approach to extend the existing toolkit for solving security games. Specifically, we present (i) OptGradFP, a novel and general algorithm that searches for the optimal defender strategy in a parameterized continuous search space, and can also be used to learn policies over multiple game states simultaneously; (ii) OptGradFP-NN, a convolutional neural network based implementation of OptGradFP for continuous space security games. We demonstrate the potential to predict good defender strategies via experiments and analysis of OptGradFP and OptGradFP-NN on discrete and continuous game settings.

Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 personyears of gonorrhea annually compared to current policy.

We consider the problem of allocating scarce security resources among heterogeneous targets to thwart a possible attack. It is well known that deterministic solutions to this problem being highly predictable are severely suboptimal. To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary. Unfortunately, such mixed strategies typically randomize over a large number of strategies, requiring security personnel to be familiar with numerous protocols, making them hard to operationalize. Motivated by these practical considerations, we propose an easy to use approach for computing strategies that are easy to operationalize and that bridge the gap between the static solution and the optimal mixed strategy. These strategies only randomize over an optimally chosen subset of pure strategies whose cardinality is selected by the defender, enabling them to conveniently tune the trade-off between ease of operationalization and efficiency using a single design parameter. We show that the problem of computing such operationalizable strategies is NP-hard, formulate it as a mixedinteger optimization problem, provide an algorithm for computing ✏-optimal equilibria, and an efficient heuristic. We evaluate the performance of our approach on the problem of screening for threats at airport checkpoints and show that the Price of Usability, i.e., the loss in optimality to obtain a strategy that is easier to operationalize, is typically not high.

The conditional value at risk (CVaR) is a popular risk measure which enables risk-averse decision making under uncertainty. We consider maximizing the CVaR of a continuous submodular function, an extension of submodular set functions to a continuous domain. One example application is allocating a continuous amount of energy to each sensor in a network, with the goal of detecting intrusion or contamination. Previous work allows maximization of the CVaR of a linear or concave function. Continuous submodularity represents a natural set of nonconcave functions with diminishing returns, to which existing techniques do not apply. We give a (1 − 1/e)-approximation algorithm for maximizing the CVaR of a monotone continuous submodular function. This also yields an algorithm for submodular set functions which produces a distribution over feasible sets with guaranteed CVaR. Experimental results in two sensor placement domains confirm that our algorithm substantially outperforms competitive baselines.

We consider the problem of monitoring a set of targets, using scarce monitoring resources (e.g., sensors) that are subject to adversarial attacks. In particular, we propose a constant-sum Stackelberg game in which a defender (leader) chooses among possible monitoring locations, each covering a subset of targets, while taking into account the monitor failures induced by a resource-constrained attacker (follower). In contrast to the previous Stackelberg security models in which the defender uses mixed strategies, here, the defender must commit to pure strategies. This problem is highly intractable as both players’ strategy sets are exponentially large. Thus, we propose a solution methodology that automatically partitions the set of adversary’s strategies and maps each subset to a coverage policy. These policies are such that they do not overestimate the defender’s payoff. We show that the partitioning problem can be reformulated exactly as a mixed-integer linear program (MILP) of moderate size which can be solved with off-the-shelf solvers. We demonstrate the effectiveness of our proposed approach in various settings. In particular, we illustrate that even with few policies, we are able to closely approximate the optimal solution and outperform the heuristic solutions.

Stackelberg Security Games (SSGs) have been adopted widely for modeling adversarial interactions, wherein scalability of equilibrium computation is an important research problem. While prior research has made progress with regards to scalability, many real world problems cannot be solved satisfactorily yet as per current requirements; these include the deployed federal air marshals (FAMS) application and the threat screening (TSG) problem at airports. We initiate a principled study of approximations in zero-sum SSGs. Our contribution includes the following: (1) a unified model of SSGs called adversarial randomized allocation (ARA) games, (2) hardness of approximation for zero-sum ARA, as well as for the FAMS and TSG sub-problems, (3) an approximation framework for zero-sum ARA with instantiations for FAMS and TSG using intelligent heuristics, and (4) experiments demonstrating the significant 1000x improvement in runtime with an acceptable loss.

Traditional security games concern the optimal randomized allocation of human patrollers, who can directly catch attackers or interdict attacks. Motivated by the emerging application of utilizing mobile sensors (e.g., UAVs) for patrolling, in this paper we propose the novel Sensor-Empowered security Game (SEG) model which captures the joint allocation of human patrollers and mobile sensors. Sensors differ from patrollers in that they cannot directly interdict attacks, but they can notify nearby patrollers (if any). Moreover, SEGs incorporate mobile sensors’ natural functionality of strategic signaling. On the technical side, we first prove that solving SEGs is NP-hard even in zero-sum cases. We then develop a scalable algorithm SEGer based on the branch-and-price framework with two key novelties: (1) a novel MILP formulation for the slave; (2) an efficient relaxation of the problem for pruning. To further accelerate SEGer, we design a faster combinatorial algorithm for the slave problem, which is provably a constant-approximation to the slave problem in zerosum cases and serves as a useful heuristic for general-sum SEGs. Our experiments demonstrate the significant benefit of utilizing mobile sensors.

While reigning models of diffusion have privileged the structure of a given social network as the key to informational exchange, real human interactions do not appear to take place on a single graph of connections. Using data collected from a pilot study of the spread of HIV awareness in social networks of homeless youth, we show that health information did not diffuse in the field according to the processes outlined by dominant models. Since physical network diffusion scenarios often diverge from their more well-studied counterparts on digital networks, we propose an alternative Activation Jump Model (AJM) that describes information diffusion on physical networks from a multi-agent team perspective. Our model exhibits two main differentiating features from leading cascade and threshold models of influence spread: 1) The structural composition of a seed set team impacts each individual node’s influencing behavior, and 2) an influencing node may spread information to non-neighbors. We show that the AJM significantly outperforms existing models in its fit to the observed node-level influence data on the youth networks. We then prove theoretical results, showing that the AJM exhibits many well-behaved properties shared by dominant models. Our results suggest that the AJM presents a flexible and more accurate model of network diffusion that may better inform influence maximization in the field.

Poachers are engaged in extinction level wholesale slaughter, so it is critical to harness historical data for predicting poachers’ behavior. However, in these domains, data collected about adversarial actions are remarkably imperfect, where reported negative instances of crime may be mislabeled or uncertain. Unfortunately, past attempts to develop predictive and prescriptive models to address this problem suffer from shortcomings from a modeling perspective as well as in the implementability of their techniques. Most notably these models i) neglect the uncertainty in crime data, leading to inaccurate and biased predictions of adversary behavior, ii) use coarse-grained crime analysis and iii) do not provide a convincing evaluation as they only look at a single protected area. Additionally, they iv) proposed time-consuming techniques which cannot be directly integrated into low resource outposts. In this innovative application paper, we (I) introduce iWare-E a novel imperfect-observation aWare Ensemble (iWare-E) technique, which is designed to handle the uncertainty in crime information efficiently. This approach leads to superior accuracy and efficiency for adversary behavior prediction compared to the previous stateof-the-art. We also demonstrate the country-wide efficiency of the models and are the first to (II) evaluate our adversary behavioral model across different protected areas in Uganda, i.e., Murchison Fall and Queen Elizabeth National Park, (totaling about 7500 km2) as well as (III) on fine-grained temporal resolutions. Lastly, (IV) we provide a scalable planning algorithm to design fine-grained patrol routes for the rangers, which achieves up to 150% improvement in number of predicted attacks detected.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key ”seed” nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ∼160% in terms of information spread about HIV among homeless youth.

This work is aims to overcome the challenges in deploying influence maximization to support community driven interventions. Influence maximization is a crucial technique used in preventative health interventions, such as HIV prevention amongst homeless youth. Drop-in centers for homeless youth train a subset of youth as peer leaders who will disseminate information about HIV through their social networks. The challenge is to find a small set of peer leaders who will have the greatest possible influence. While many algorithms have been proposed for influence maximization, none can be feasibly deployed by a service provider: existing algorithms require costly surveys of the entire social network of the youth to provide input data, and high performance computing resources to run the algorithm itself. Both requirements are crucial bottlenecks to widespread use of influence maximization in real world interventions. To address the above challenges, this innovative applications paper introduces the CHANGE agent for influence maximization. CHANGE handles the end-to-end process of influence maximization, from data collection to peer leader selection. Crucially, CHANGE only surveys a fraction of the youth to gather network data and minimizes computational cost while providing comparable performance to previously proposed algorithms. We carried out a pilot study of CHANGE in collaboration with a drop-in center serving homeless youth in a major U.S. city. CHANGE surveyed only 18% of the youth to construct its social network. However, the peer leaders it selected reached just as many youth as previously field-tested algorithms which surveyed the entire network. This is the first real-world study of a network sampling algorithm for influence maximization. Simulation results on real-world networks also support our claims.

In many real world applications of influence maximization, practitioners intervene in a population whose social structure is initially unknown. This poses a multiagent systems challenge to act under uncertainty about how the agents are connected. We formalize this problem by introducing exploratory influence maximization, in which an algorithm queries individual network nodes (agents) to learn their links. The goal is to locate a seed set nearly as influential as the global optimum using very few queries. We show that this problem is intractable for general graphs. However, real world networks typically have community structure, where nodes are arranged in densely connected subgroups. We present the ARISEN algorithm, which leverages community structure to find an influential seed set. Experiments on real world networks of homeless youth, village populations in India, and others demonstrate ARISEN’s strong empirical performance. To formally demonstrate how ARISEN exploits community structure, we prove an approximation guarantee for ARISEN on graphs drawn from the Stochastic Block Model.

In Stackelberg security games, a defender seeks to randomly allocate limited security resources to protect critical targets from an attack. In this paper, we study a fundamental, yet underexplored, phenomenon in security games, which we term the Curse of Correlation (CoC). Specifically, we observe that there are inevitable correlations among the protection status of different targets. Such correlation is a crucial concern, especially in spatio-temporal domains like conservation area patrolling, where attackers can surveil patrollers at certain areas and then infer their patrolling routes using such correlations. To mitigate this issue, we propose to design entropy-maximizing defending strategies for spatio-temporal security games, which frequently suffer from CoC. We prove that the problem is #P-hard in general. However, it admits efficient algorithms in well-motivated special settings.

Diseases such as heart disease, stroke, or diabetes affect hundreds of millions of people. Such conditions are strongly impacted by obesity, and establishing healthy lifestyle behaviors is a critical public health challenge with many applications. Changing health behaviors is inherently a multiagent problem since people’s behavior is strongly influenced by those around them. Hence, practitioners often attempt to modify the social network of a community by adding or removing edges in ways that will lead to desirable behavior change. To our knowledge, no previous work considers the algorithmic problem of finding the optimal set of edges to add and remove. We propose the RECONNECT algorithm, which efficiently finds high-quality solutions for a range of different network intervention problems. We evaluate RECONNECT in a highly realistic simulated environment based on the Antelope Valley region in California which draws on demographic, social, and health-related data. We find the RECONNECT outperforms an array of baseline policies, in some cases yielding a 150% improvement over the best alternative.

Diseases such as heart disease, stroke, or diabetes affect hundreds of millions of people. Such conditions are strongly impacted by obesity, and establishing healthy lifestyle behaviors is a critical public health challenge with many applications. Changing health behaviors is inherently a multiagent problem since people’s behavior is strongly influenced by those around them. Hence, practitioners often attempt to modify the social network of a community by adding or removing edges in ways that will lead to desirable behavior change. To our knowledge, no previous work considers the algorithmic problem of finding the optimal set of edges to add and remove. We propose the RECONNECT algorithm, which efficiently finds high-quality solutions for a range of different network intervention problems. We evaluate RECONNECT in a highly realistic simulated environment based on the Antelope Valley region in California which draws on demographic, social, and health-related data. We find the RECONNECT outperforms an array of baseline policies, in some cases yielding a 150% improvement over the best alternative.