Bryan Wilder and Yevgeniy Vorobeychik. 2018. “Controlling Elections through Social Influence.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
Election control considers the problem of an adversary who attempts to tamper with a voting process, in order to either ensure that their favored candidate wins (constructive control) or another candidate loses (destructive control). As online social networks have become significant sources of information for potential voters, a new tool in an attacker’s arsenal is to effect control by harnessing social influence, for example, by spreading fake news and other forms of misinformation through online social media. We consider the computational problem of election control via social influence, studying the conditions under which finding good adversarial strategies is computationally feasible. We consider two objectives for the adversary in both the constructive and destructive control settings: probability and margin of victory (POV and MOV, respectively). We present several strong negative results, showing, for example, that the problem of maximizing POV is inapproximable for any constant factor. On the other hand, we present approximation algorithms which provide somewhat weaker approximation guarantees, such as bicriteria approximations for the POV objective and constant-factor approximations for MOV. Finally, we present mixed integer programming formulations for these problems. Experimental results show that our approximation algorithms often find near-optimal control strategies, indicating that election control through social influence is a salient threat to election integrity.
Aaron Schlenker, Omkar Thakoor, Haifeng Xu, Fei Fang, Milind Tambe, Long Tran-Thanh, Phebe Vayanos, and Yevgeniy Vorobeychik. 2018. “Deceiving Cyber Adversaries: A Game Theoretic Approach .” In .Abstract
An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game-theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender’s exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches.
Mohammad Javad Azizi, Phebe Vayanos, Bryan Wilder, Eric Rice, and Milind Tambe. 2018. “Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources.” In International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research.Abstract
We consider the problem of designing fair, efficient, and interpretable policies for prioritizing heterogeneous homeless youth on a waiting list for scarce housing resources of different types. We focus on point-based policies that use features of the housing resources (e.g., permanent supportive housing, rapid rehousing) and the youth (e.g., age, history of substance use) to maximize the probability that the youth will have a safe and stable exit from the housing program. The policies can be used to prioritize waitlisted youth each time a housing resource is procured. Our framework provides the policy-maker the flexibility to select both their desired structure for the policy and their desired fairness requirements. Our approach can thus explicitly trade-off interpretability and efficiency while ensuring that fairness constraints are met. We propose a flexible data-driven mixed-integer optimization formulation for designing the policy, along with an approximate formulation which can be solved efficiently for broad classes of interpretable policies using Bender’s decomposition. We evaluate our framework using real-world data from the United States homeless youth housing system. We show that our framework results in policies that are more fair than the current policy in place and than classical interpretable machine learning approaches while achieving a similar (or higher) level of overall efficiency.
Bryan Wilder. 2018. “Equilibrium Computation and Robust Optimization in Zero Sum Games with Submodular Structure .” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
We define a class of zero-sum games with combinatorial structure, where the best response problem of one player is to maximize a submodular function. For example, this class includes security games played on networks, as well as the problem of robustly optimizing a submodular function over the worst case from a set of scenarios. The challenge in computing equilibria is that both players’ strategy spaces can be exponentially large. Accordingly, previous algorithms have worst-case exponential runtime and indeed fail to scale up on practical instances. We provide a pseudopolynomial-time algorithm which obtains a guaranteed (1 − 1/e) 2 -approximate mixed strategy for the maximizing player. Our algorithm only requires access to a weakened version of a best response oracle for the minimizing player which runs in polynomial time. Experimental results for network security games and a robust budget allocation problem confirm that our algorithm delivers near-optimal solutions and scales to much larger instances than was previously possible.
Kai Wang, Qingyu Guo, Phebe Vayanos, Milind Tambe, and Bo An. 2018. “Equilibrium Refinement in Security Games with Arbitrary Scheduling Constraints .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
Significant research effort in security games has focused in devising strategies that perform well even when the attacker deviates from optimal (rational) behavior. In most of these frameworks, a price needs to be paid to ensure robustness against this unpredictability. However, equilibrium refinement is an attractive alternative to boost solution robustness at no cost even though it has not received as much attention in security game literature. In this framework, resources are strategically allocated to secure an optimal outcome against a rational adversary while simultaneously protecting other targets to ensure good outcomes against boundedly rational or constrained attackers. Unfortunately, existing approaches for equilibrium refinement in security games cannot effectively address scheduling constraints that arise frequently in real-world applications. In this paper, we aim to fill this gap and make several key contributions. First, we show that existing approaches for equilibrium refinement can fail in the presence of scheduling constraints. Second, we investigate the properties of the best response of the attacker. Third, we leverage these properties to devise novel iterative algorithms to compute the optimally refined equilibrium, with polynomially many calls to an LP oracle for zero-sum games. Finally, we conduct extensive experimental evaluations that showcase i) the superior performance of our approach in the face of a boundedly rational attacker and ii) the attractive scalability properties of our algorithm that can solve realistic-sized instances.
Hau Chan, Eric Rice, Phebe Vayanos, Milind Tambe, and Matthew Morton. 2018. “From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth .” In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).Abstract
There are nearly 2 million homeless youth in the United States each year. Coordinated entry systems are being used to provide homeless youth with housing assistance across the nation. Despite these efforts, the number of youth still homeless or unstably housed remains very high. Motivated by this fact, we initiate a first study to understand and analyze the current governmental housing systems for homeless youth. In this paper, we aim to provide answers to the following questions: (1) What is the current governmental housing system for assigning homeless youth to different housing assistance? (2) Can we infer the current assignment guidelines of the local housing communities? (3) What is the result and outcome of the current assignment process? (4) Can we predict whether the youth will be homeless after receiving the housing assistance? To answer these questions, we first provide an overview of the current housing systems. Next, we use simple and interpretable machine learning tools to infer the decision rules of the local communities and evaluate the outcomes of such assignment. We then determine whether the vulnerability features/rubrics can be used to predict youth’s homelessness status after receiving housing assistance. Finally, we discuss the policy recommendations from our study for the local co
Han-Ching Ou, Milind Tambe, Bistra Dilkina, and Phebe Vayanos. 2018. “Imbalanced Collusive Security Games .” In Conference on Decision and Game Theory for Security (GameSec).Abstract
Colluding adversaries is a crucial challenge for defenders in many real-world applications. Previous literature has provided Collusive Security Games (COSG) to model colluding adversaries, and provided models and algorithms to generate defender strategies to counter colluding adversaries, often by devising strategies that inhibit collusion [6]. Unfortunately, this previous work focused exclusively on situations with perfectly matched adversaries, i.e., where their rewards were symmetrically distributed. In the real world, however, defenders often face adversaries where their rewards are asymmetrically distributed. Such inherent asymmetry raises a question as to whether human adversaries would attempt to collude in such situations, and whether defender strategies to counter such collusion should focus on inhibiting collusion. To address these open questions, this paper: (i) explores and theoretically analyzes Imbalanced Collusive Security Games (ICOSG) where defenders face adversaries with asymmetrically distributed rewards; (ii) conducts extensive experiments of three different adversary models involving 1800 real human subjects and (iii) derives novel analysis of the reason behind why bounded rational attackers models outperform perfectly rational attackers models. The key principle discovered as the result of our experiments is that: careful modeling of human bounded rationality reveals a key difference (when compared to a model using perfect rationality) in defender strategies for handling colluding adversaries which face symmetric vs asymmetric rewards. Whereas a model based on perfect rationality always attempts to break collusion among adversaries, a bounded rationality model acknowledges the inherent difficulty of breaking such collusion in symmetric situations and focuses only on breaking collusion in asymmetric situation, and only on damage control from collusion in the symmetric situation.
Qingyu Guo, Jiarui Gan, Fei Fang, Long Tran-Thanh, Milind Tambe, and Bo An. 2018. “Inducible Equilibrium for Security Games (Extended Abstract) .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18) [short paper].Abstract
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.
Edward A. Cranford, Christian Lebiere, Cleotilde Gonzalez, Sarah Cooney, Phebe Vayanos, and Milind Tambe. 2018. “Learning about Cyber Deception through Simulations: Predictions of Human Decision Making with Deceptive Signals in Stackelberg Security Games .” In Annual meeting of the Cognitive Science Society (CogSci).Abstract
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.
Eric Rice, Monique Holguin, Hsun-Ta Hsu, Matthew Morton, Phebe Vayanos, Milind Tambe, and Hau Chan. 2018. “Linking Homelessness Vulnerability Assessments to Housing Placements and Outcomes for Youth .” Cityscape: A Journal of Policy Development and Research, Volume 20, Number 3, 20, 3.Abstract
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.
Elizabeth Bondi, Ashish Kapoor, Debadeepta Dey, James Piavis, Shital Shah, Robert Hannaford, Arvind Iyer, Lucas Joppa, and Milind Tambe. 2018. “Near Real-Time Detection of Poachers from Drones in AirSim .” In International Joint Conference on Artificial Intelligence (IJCAI-18).Abstract
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.
Sungyong Seo, Hau Chan, P. Jeffrey Brantingham, Jorja Leap, Phebe Vayanos, Milind Tambe, and Yan Liu. 2018. “Partially Generative Neural Networks for Gang Crime Classification with Partial Information.” In International Conference on AAAI ACM conference on AI, Ethics and Society (AIES).Abstract
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.
Nitin Kamra, Umang Gupta, Fei Fang, Yan Liu, and Milind Tambe. 2018. “Policy Learning for Continuous Space Security Games using Neural Networks .” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
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.
Bryan Wilder, Sze-Chuan Suen, and Milind Tambe. 2018. “Preventing Infectious Disease in Dynamic Populations Under Uncertainty .” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
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.
Sara Marie Mc Carthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, and Milind Tambe. 2018. “The Price of Usability: Designing Operationalizable Strategies for Security Games .” In 27th International Joint Conference on Artificial Intelligence (IJCAI).Abstract
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.
Bryan Wilder. 2018. “Risk-Sensitive Submodular Optimization .” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
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.
Aida Rahmattalabi, Phebe Vayanos, and Milind Tambe. 2018. “A Robust Optimization Approach to Designing Near-Optimal Strategies for Constant-Sum Monitoring Games .” In Conference on Decision and Game Theory for Security (GameSec).Abstract
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.
Arunesh Sinha, Aaron Schlenker, Donnabell Dmello, and Milind Tambe. 2018. “Scaling-up Stackelberg Security Games Applications using Approximations.” In Conference on Decision and Game Theory for Security (GameSec).Abstract
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.
Haifeng Xu, Kai Wang, Phebe Vayanos, and Milind Tambe. 2018. “Strategic Coordination of Human Patrollers and Mobile Sensors with Signaling for Security Games .” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
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.
Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the 'Breakfast Club': Modeling Influence Spread in Natural-World Social Networks.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
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.