Illegal smuggling is one of the most important issues across countries, where more than $10 billion
a year of illegal wildlife trafficking is conducted
within transnational criminal networks. Governments have tried to deploy inspections at checkpoints to stop illegal smuggling, though the effect
is quite limited due to vast protection areas but limited human resources. We study these problems
from the perspective of network interdiction games
with a boundedly rational attacker. In this paper, we
aim to improve the efficiency of the limited number of checkpoints. The problem involves two main
stages: i) a predictive stage to predict the attacker’s
behavior based on the historical interdiction; ii)
a prescriptive stage to optimally allocate limited
checkpoints to interdict the attacker. In this paper, we propose a novel boundedly rational model
which resolves the issue of exponentially many attacker strategies by making memoryless assumption about the attacker’s behavior. We show that
the attacker’s behavior can be reduced to an absorbing Markov chain, where the success probability of
reaching any target can be computed analytically,
thus optimized via any gradient-based optimization technique. We incorporate graph convolutional
neural networks with K-hops look-ahead to model
the attacker’s reasoning. Our proposed model provides a new perspective to study the boundedly
rationality in traditional interdiction games with
graph structure. This novel model possesses nice
analytical properties and scales up very well by
avoiding enumerating all paths in the graph.
In important domains from natural resource conservation to public safety, real-time information is becoming increasingly important. Strategic deployment of security cameras and mobile sensors such as drones can provide real-time updates on illegal activities. To help plan for such strategic deployments of sensors and human patrollers, as well as warning signals to ward off adversaries, the defender-attacker security games framework can be used. [Zhang et al., 2019] has shown that real-time data (e.g., human view from a helicopter) may be used in conjunction with security game models to interdict criminals. Other recent work relies on real-time information from sensors that can notify the patroller when an opponent is detected [Basilico et al., 2017; Xu et al., 2018]. Despite considering real-time information in all cases, these works do not consider the combined situation of uncertainty in real-time information in addition to strategically signaling to adversaries. In this thesis, we will not only address this gap, but also improve the overall security result by considering security game models and computer vision algorithms together. A major aspect of this work is in applying it to real-world challenges, such as conservation. Although it applies to many environmental challenges, such as protecting forests and avoiding illegal mining, we will focus particularly on reducing poaching of endangered wildlife as an example. To reduce poaching, human patrollers typically search for snares and poaching activity as they patrol, as well as intervene if poaching activity is found. Drones are useful patrolling aids due to their ability to cover additional ground, but they must interpret their environments, notify nearby human patrollers for intervention, and send potentially deceptive signals to the adversary to deter poaching. Rather than treating these as separate tasks, models must coordinate to handle challenges found in real-world conservation scenarios (Fig. 1). We will determine the success of this work both in simulated experiments and through work with conservation agencies such as Air Shepherd to implement the system in the real world.
Defender-attacker Stackelberg security games (SSGs) have been applied for solving many real-world security problems. Recent work in SSGs has incorporated a deceptive signaling scheme into the SSG model, where the defender strategically reveals information about her defensive strategy to the attacker, in order to inuence the attacker’s decision making for the defender’s own benet. In this work, we study the problem of signaling in security games against a boundedly rational attacker.
Controlling recurrent infectious diseases is a vital yet complicated problem. In this paper, we propose a novel active screening model (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under infection uncertainty. Our contributions are: (1) A new approach to modeling multi-round networkbased screening/contact tracing under uncertainty, which is a common real-life practice in a variety of diseases [10, 30]; (2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and finds a policy that provides high solution quality, where Fast-REMEDY scales linearly in the size of the network; (3) We evaluate Full- and Fast-REMEDY on several real-world datasets which emulate human contact and find that they control diseases better than the baselines. To the best of our knowledge, this is the first work on multiround active screening with uncertainty for diseases with no permanent immunity.
Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our CLUSTERNET system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization. Code for our system is available at https://github.com/bwilder0/clusternet.
Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings subject to unanticipated challenges with complex social effects. Motivated by real-world deployment of AI driven, social-network based suicide prevention and landslide risk management interventions, this paper focuses on robust graph covering problems subject to group fairness constraints. We show that, in the absence of fairness constraints, state-of-the-art algorithms for the robust graph covering problem result in biased node coverage: they tend to discriminate individuals (nodes) based on membership in traditionally marginalized groups. To mitigate this issue, we propose a novel formulation of the robust graph covering problem with group fairness constraints and a tractable approximation scheme applicable to real-world instances. We provide a formal analysis of the price of group fairness (PoF) for this problem, where we show that uncertainty can lead to greater PoF. We demonstrate the effectiveness of our approach on several real-world social networks. Our method yields competitive node coverage while significantly improving group fairness relative to state-of-the-art methods.
Substance use and abuse is a significant public health problem in the United States. Group-based intervention programs offer a promising means of preventing and reducing substance abuse. While effective, unfortunately, inappropriate intervention groups can result in an increase in deviant behaviors among participants, a process known as deviancy training. This paper investigates the problem of optimizing the social influence related to the deviant behavior via careful construction of the intervention groups. We propose a Mixed Integer Optimization formulation that decides on the intervention groups to be formed, captures the impact of the intervention groups on the structure of the social network, and models the impact of these changes on behavior propagation. In addition, we propose a scalable hybrid meta-heuristic algorithm that combines Mixed Integer Programming and Large Neighborhood Search to find nearoptimal network partitions. Our algorithm is packaged in the form of GUIDE, an AI-based decision aid that recommends intervention groups. Being the first quantitative decision aid of this kind, GUIDE is able to assist practitioners, in particular social workers, in three key areas: (a) GUIDE proposes near-optimal solutions that are shown, via extensive simulations, to significantly improve over the traditional qualitative practices for forming intervention groups; (b) GUIDE is able to identify circumstances when an intervention will lead to deviancy training, thus saving time, money, and effort; (c) GUIDE can evaluate current strategies of group formation and discard strategies that will lead to deviancy training. In developing GUIDE, we are primarily interested in substance use interventions among homeless youth as a high risk and vulnerable population. GUIDE is developed in collaboration with Urban Peak, a homelessyouth serving organization in Denver, CO, and is under preparation for deployment.
Most of the current security models assume that the values of targets/areas are static or the changes (if any) are scheduled and known to the defender. Unfortunately, such models are not sufficient for many domains, where actions of the players modify the values of the targets. Examples include wildlife scenarios, where the attacker can increase value of targets by secretly building supporting facilities. To address such security game domains with player-affected values, we first propose DPOS3G, a novel partially observable stochastic Stackelberg game where target values are determined by the players’ actions; the defender can only partially observe these targets’ values, while the attacker can fully observe the targets’ values and the defender’s strategy. Second, we propose RITA (Reduced game Iterative Transfer Algorithm), which is based on the heuristic search value iteration algorithm for partially observable stochastic game (PG-HSVI) and introduces three key novelties: (a) building a reduced game with only key states (derived from partitioning the state space) to reduce the numbers of states and transitions considered when solving the game; (b) incrementally adding defender’s actions to further reduce the number of transitions of the game; (c) providing novel heuristics for lower bound initialization of the algorithm. Third, we conduct extensive experimental evaluations of the algorithms and the results show that RITA significantly outperforms the baseline PG-HSVI algorithm on scalability while allowing for trade off in scalability and solution quality.