Tambe, Milind


SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

AAAI Conferences

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.


Influence Maximization for Social Network Based Substance Abuse Prevention

AAAI Conferences

Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of reducing substance abuse. While effective, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. In this paper, we present GUIDE, an AI-based decision aid that leverages social network information to optimize the structure of the intervention groups.


Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games

AAAI Conferences

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 zero-sum cases and serves as a useful heuristic for general-sum SEGs. Our experiments demonstrate the significant benefit of utilizing mobile sensors.


Preventing Infectious Disease in Dynamic Populations Under Uncertainty

AAAI Conferences

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 person-years of gonorrhea annually compared to current policy.


Policy Learning for Continuous Space Security Games Using Neural Networks

AAAI Conferences

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.


Maximizing Influence in an Unknown Social Network

AAAI Conferences

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.


Mitigating the Curse of Correlation in Security Games by Entropy Maximization

arXiv.org Artificial Intelligence

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 \emph{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 \emph{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. Our experiments show significant advantages of max-entropy algorithms over previous algorithms. A scalable implementation of our algorithm is currently under pre-deployment testing for integration into FAMS software to improve the scheduling of US federal air marshals.


Evidence From the Past: AI Decision Aids to Improve Housing Systems for Homeless Youth

AAAI Conferences

Could an AI decision aid improve housing systems that assist homeless youth? 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 homeless youth still homeless and unstably housed on the street remains very high. Motivated by this fact, we initiate a first study to create AI decision aids for improving the current housing systems for homeless youth. First, we determine whether the current rubric for prioritizing youth for housing assistance can be used to predict youth's homelessness status after receiving housing assistance. We then consider building better AI decision aids and predictive models using other components of the rubric. We believe there is much potential for effective human-machine collaboration in the context of housing allocation. We plan to work with HUD and local communities to develop such systems in the future.


PSINET: Assisting HIV Prevention Amongst Homeless Youth by Planning Ahead

AI Magazine

Homeless youth are prone to Human Immunodeficiency Virus (HIV) due to their engagement in high risk behavior such as unprotected sex, sex under influence of drugs, and so on. Many non-profit agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization; and (3) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60 percent more information spread over the current state of the art. PSINET was developed in collaboration with My Friend’s Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.


Conquering Adversary Behavioral Uncertainty in Security Games: An Efficient Modeling Robust Based Algorithm

AAAI Conferences

Stackelberg Security Games (SSG) have been widely applied for solving real-world security problems—with a significant research emphasis on modeling attackers’ behaviors to handle their bounded rationality. However, access to real-world data (used for learning an accurate behavioral model) is often limited, leading to uncertainty in attacker’s behaviors while modeling. This paper therefore focuses on addressing behavioral uncertainty in SSG with the following main contributions: 1) we present a new uncertainty game model that integrates uncertainty intervals into a behavioral model to capture behavioral uncertainty; 2) based on this game model, we propose a novel robust algorithm that approximately computes the defender’s optimal strategy in the worst-case scenario of uncertainty—with a bound guarantee on its solution quality.