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.
Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, and Aggrey Rwetsiba. 2018. “Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version).” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2018).Abstract
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.
Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2018. “Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth.” In International Joint Conference on Artificial Intelligence (IJCAI).Abstract
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.
Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, and Eric Rice. 2018. “End-to-End Influence Maximization in the Field.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
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.
Bryan Wilder, Nicole Immorlica, Eric Rice, and Milind Tambe. 2018. “Maximizing Influence in an Unknown Social Network.” In AAAI conference on Artificial Intelligence (AAAI-18).Abstract
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.
Haifeng Xu, Shaddin Dughmi, Milind Tambe, and Venil Loyd Noronha. 2018. “Mitigating the Curse of Correlation in Security Games by Entropy Maximization (Extended Abstract).” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
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.
Bryan Wilder, Han Ching Ou, Kayla de la Haye, and Milind Tambe. 2018. “Optimizing network structure for preventative health.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
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.
Bryan Wilder, Han Ching Ou, Kayla de la Haye, and Milind Tambe. 2018. “Optimizing network structure for preventative health.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
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.
Eric Rice, Amanda Yoshioka-Maxwell, Robin Petering, Laura Onasch-Vera, Jaih Craddock, Milind Tambe, Amulya Yadav, Bryan Wilder, Darlene Woo, Hailey Winetrobe, and Nicole Wilson. 2018. “Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness.” Journal of the Society for Social Work and Research, Volume 9, Number 4., 9, 4.Abstract
Youth experiencing homelessness are at risk for HIV and need interventions to prevent risky sex behaviors. We tested the feasibility of using artificial intelligence (AI) to select peer change agents (PCAs) to deliver HIV prevention messages among youth experiencing homelessness. Method: We used a pretest– posttest quasi-experimental design. In the AI condition (n 5 62), 11 PCAs were selected via AI algorithm; in the popularity comparison (n 5 55), 11 PCAs were selected 6 months later based on maximum degree centrality (most ties to others in the network). All PCAs were trained to promote HIV testing and condom use among their peers. Participants were clients at a drop-in center in Los Angeles, CA. HIV testing and condom use were assessed via a self-administered, computer-based survey at baseline (n 5 117), 1 month (n 5 86, 74%), and 3 months (n 5 70, 60%). Results: At 3 months, rates of HIV testing increased among participants in the AI condition relative to the comparison group (18.8% vs. 8.1%), as did condom use during anal sex (12.1% vs. 3.3%) and vaginal sex (29.2% vs. 23.7%). Conclusions: AI-enhanced PCA intervention is a feasible method for engaging youth experiencing homelessness in HIV prevention
Amulya Yadav, Ritesh Noothigattu, Eric Rice, Laura Onasch-Vera, Leandro Marcolino, and Milind Tambe. 2018. “Please be an influencer? Contingency Aware Influence Maximization.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
Most previous work on influence maximization in social networks assumes that the chosen influencers (or seed nodes) can be influenced with certainty (i.e., with no contingencies). In this paper, we focus on using influence maximization in public health domains for assisting low-resource communities, where contingencies are common. It is very difficult in these domains to ensure that the seed nodes are influenced, as influencing them entails contacting/convincing them to attend training sessions, which may not always be possible. Unfortunately, previous state-of-the-art algorithms for influence maximization are unusable in this setting. This paper tackles this challenge via the following four contributions: (i) we propose the Contingency Aware Influence Maximization problem and analyze it theoretically; (ii) we cast this problem as a Partially Observable Markov Decision Process and propose CAIMS (a novel POMDP planner) to solve it, which leverages a natural action space factorization associated with real-world social networks; and (iii) we provide extensive simulation results to compare CAIMS with existing state-of-the-art influence maximization algorithms. Finally, (iv) we provide results from a real-world feasibility trial conducted to evaluate CAIMS, in which key influencers in homeless youth social networks were influenced in order to spread awareness about HIV.
Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Jopp, Milind Tambe, and Ram Nevatia. 2018. “SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time.” Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-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 long wave 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 animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we build SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time. 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 and architecture of SPOT, (ii) a series of efforts towards more robust and faster processing to make SPOT usable in the field and provide detections in near real time, and (iii) evaluation of SPOT based on both historical videos and a real-world test run by the end users in the field. The promising results from the test in the field have led to a plan for larger-scale deployment in a national park in Botswana. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.
Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, and Milind Tambe. 2018. “Stackelberg Security Games: Looking Beyond a Decade of Success.” In International Joint Conference on Artificial Intelligence (IJCAI).Abstract
The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.