Influence Maximization is an active topic, but it was
always assumed full knowledge of the social network
graph. However, the graph may actually be unknown
beforehand. For example, when selecting a subset of a
homeless population to attend interventions concerning
health, we deal with a network that is not fully known.
Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph.
We study a class of algorithms, where we show that: (i)
traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when
the independence of objectives hypothesis holds; (iii)
when it does not hold, the upper bound for the influence
loss converges to 0. We run extensive experiments over
four real-life social networks, where we study two alternative models, and obtain significantly better results in
both than traditional approaches.
Several models have been proposed for Stackelberg security
games (SSGs) and protection against perfectly rational and
bounded rational adversaries; however, none of these existing models addressed the destructive cooperation mechanism between adversaries. SPECTRE (Strategic Patrol
planner to Extinguish Collusive ThREats) takes into account the synergistic destructive collusion among two groups
of adversaries in security games. This framework is designed for the purpose of efficient patrol scheduling for security agents in security games in presence of collusion and
is mainly build up on game theoretic approaches, optimization techniques, machine learning methods and theories for
human decision making under risk. The major advantage of
SPECTRE is involving real world data from human subject
experiments with participants on Amazon Mechanical Turk
State-of-the-art applications of Stackelberg security games — including wildlife protection — offer a wealth of data, which can be used to learn
the behavior of the adversary. But existing approaches either make strong assumptions about the
structure of the data, or gather new data through
online algorithms that are likely to play severely
suboptimal strategies. We develop a new approach
to learning the parameters of the behavioral model
of a bounded rational attacker (thereby pinpointing
a near optimal strategy), by observing how the attacker responds to only three defender strategies.
We also validate our approach using experiments
on real and synthetic data.
Thanh H. Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, and Milind Tambe. 2016. “Towards a Science of Security Games .” In New Frontiers of Multidisciplinary Research in STEAM-H (Book chapter) (edited by B Toni).Abstract
Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Computational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of security resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games”. The research challenges posed by these applications include scaling up security games to real-world sized problems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries.
Game theory has been successfully used to handle complex resource allocation and patrolling
problems in security and sustainability domains. More specifically, real-world applications have
been deployed for different domains based on the framework of security games, where the defender (e.g., security agency) has a limited number of resources to protect a set of targets from
an adversary (e.g., terrorist). Whereas the first generation of security games research provided
algorithms for optimizing security resources in mostly static settings, my thesis advances the
state-of-the-art to a new generation of security games, handling massive games with complex
spatio-temporal settings and leading to real-world applications that have fundamentally altered
current practices of security resource allocation. Indeed, in many real-world domains, players act
in a geographical space over time, and my thesis is then to expand the frontiers of security games
and to deal with challenges in domains with spatio-temporal dynamics. My thesis provides the
first algorithms and models for advancing key aspects of spatio-temporal challenges in security
games, including (i) continuous time; (ii) continuous space; (iii) frequent and repeated attacks;
(iv) complex spatial constraints.
First, focusing on games where actions are taken over continuous time (for example games
with moving targets such as ferries and refugee supply lines), I propose a new game model that
accurately models the continuous strategy space for the attacker. Based on this model, I provide an efficient algorithm to calculate the defender’s optimal strategy using a compact representation
for both the defender and the attacker’s strategy space. Second, for games where actions are taken
over continuous space (for example games with forest land as a target), I provide an algorithm
computing the optimal distribution of patrol effort. Third, my work addresses challenges with one
key dimension of complexity – frequent and repeated attacks. Motivated by the repeated interaction of players in domains such as preventing poaching and illegal fishing, I introduce a novel
game model that deals with frequent defender-adversary interactions and provide algorithms to
plan effective sequential defender strategies. Furthermore, I handle complex spatial constraints
that arise from the problem of designing optimal patrol strategy given detailed topographical
My thesis work has led to two applications which have been deployed in the real world and
have fundamentally altered previously used tactics, including one used by the US Coast Guard
for protecting the Staten Island Ferry in New York City and another deployed in a protected area
in Southeast Asia to combat poaching.
Wildlife poaching presents a serious extinction threat to many animal species. Agencies (“defenders”) focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous security games research. To that end, we have built a new predictive anti-poaching tool, CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning). CAPTURE provides four main contributions. First, CAPTURE’s modeling of poachers provides significant advances over previous models from behavioral game theory and conservation biology. This accounts for: (i) the defender’s imperfect detection of poaching signs; (ii) complex temporal dependencies in the poacher’s behaviors; (iii) lack of knowledge of numbers of poachers. Second, we provide two new heuristics: parameter separation and target abstraction to reduce the computational complexity in learning the poacher models. Third, we present a new game-theoretic algorithm for computing the defender’s optimal patrolling given the complex poacher model. Finally, we present detailed models and analysis of realworld poaching data collected over 12 years in Queen Elizabeth National Park in Uganda to evaluate our new model’s prediction accuracy. This paper thus presents the largest dataset of real-world defender-adversary interactions analyzed in the security games literature. CAPTURE will be tested in Uganda in early 2016.
Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (“security games”) decision aid to optimize the use of patrolling resources. This paper reports on PAWS’s significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.
Adaptive software agents like HEALER have been proposed in the literature recently to recommend intervention plans to homeless shelter officials. However, generating networks for HEALER’s input is challenging. Moreover, HEALER’s solutions are often counter-intuitive to people. This demo paper makes two contributions. First, we demonstrate HEALER’s Facebook application, which parses the Facebook contact lists in order to construct an approximate social network for HEALER. Second, we present a software interface to run human subject experiments (HSE) to understand human biases in recommendation of intervention plans. We plan to use data collected from these HSEs to build an explanation system for HEALER’s solutions.
This paper looks at challenges faced during the ongoing deployment of HEALER, a POMDP based software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. In order to compute its plans, HEALER (i) casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) and constructs social networks of homeless youth at low cost, using a Facebook application. HEALER is currently being deployed in the real world in collaboration with a homeless shelter. Initial feedback from the shelter officials has been positive but they were surprised by the solutions generated by HEALER as these solutions are very counterintuitive. Therefore, there is a need to justify HEALER’s solutions in a way that mirrors the officials’ intuition. In this paper, we report on progress made towards HEALER’s deployment and detail first steps taken to tackle the issue of explaining HEALER’s solutions.
Green security – protection of forests, fish and wildlife – is a critical problem in environmental sustainability. We focus on the problem of optimizing the defense of forests against illegal logging, where often we are faced with the challenge of teaming up many different groups, from national police to forest guards to NGOs, each with differing capabilities and costs. This paper introduces a new, yet fundamental problem: Simultaneous Optimization of Resource Teams and Tactics (SORT). SORT contrasts with most previous game-theoretic research for green security – in particular based on security games – that has solely focused on optimizing patrolling tactics, without consideration of team formation or coordination. We develop new models and scalable algorithms to apply SORT towards illegal logging in large forest areas. We evaluate our methods on a variety of synthetic examples, as well as a real-world case study using data from our on-going collaboration in Madagascar.
Influencing a social network is an important technique, with potential to positively impact society, as we can modify the behavior of a community. For example, we can increase the overall health of a population; Yadav et al. (2015) , for instance, spread information about HIV prevention in homeless populations. However, although influence maximization has been extensively studied [2, 1], their main motivation is viral marketing, and hence they assume that the social network graph is fully known, generally taken from some social media network. However, the graphs recorded in social media do not really represent all the people and all the connections of a population. Most critically, when performing interventions in real life, we deal with large degrees of lack of knowledge. Normally the social agencies have to perform several interviews in order to learn the social network graph . These highly unknown networks, however, are exactly the ones we need to influence in order to have a positive impact in the real world, beyond product advertisement. Additionally, learning a social network graph is very valuable per se. Agencies need data about a population, in order to perform future actions to enhance their well-being, and better actuate in their practices . As mentioned, however, the works in influence maximization are currently ignoring this problem. Each person in a social network actually knows other people, including the ones she cannot directly influence. When we select someone for an intervention (to spread influence), we also have an opportunity to obtain knowledge. Therefore, in this work we present for the first time the problem of simultaneously influencing and mapping a social network. We study the performance of the classical influence maximization algorithm in this context, and show that it can be arbitrarily low. Hence, we study a class of algorithms for this problem, performing an experimentation using four real life networks of homeless populations. We show that our algorithm is competitive with previous approaches in terms of influence, and is significantly better in terms of mapping.
Security agencies including the US Coast Guard, the Federal Air Marshal Service and the Los Angeles Airport police are several major domains that have been deploying Stackelberg security games and related algorithms to protect against a single adversary or multiple, independent adversaries strategically. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender. Given the potential negative effect of these collusive actions, the defender has an incentive to break up collusion by playing off the self-interest of individual adversaries. This paper deals with problem of collusive security games for rational and bounded rational adversaries. The theoretical results verified with human subject experiments showed that behavior model which optimizes against bounded rational adversaries provides demonstrably better performing defender strategies against human subjects.
In this paper, we aim to deter urban crime by recommending optimal police patrol strategies against opportunistic criminals in large scale urban problems. While previous work has tried to learn criminals’ behavior from real world data and generate patrol strategies against opportunistic crimes, it cannot scale up to large-scale urban problems. Our first contribution is a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. In this game abstraction framework, we model the interaction between officers and opportunistic criminals as a game with discrete targets. By merging similar targets, we obtain an abstract game with fewer total targets. We use real world data to learn and plan against opportunistic criminals in this abstract game, and then propagate the results of this abstract game back to the original game. Our second contribution is the layer-generating algorithm used to merge targets as described in the framework above. This algorithm applies a mixed integer linear program (MILP) to merge similar and geographically neighboring targets in the large scale problem. As our third contribution, we propose a planning algorithm that recommends a mixed strategy against opportunistic criminals. Finally, our fourth contribution is a heuristic propagation model to handle the problem of limited data we occasionally encounter in largescale problems. As part of our collaboration with local police departments, we apply our model in two large scale urban problems: a university campus and a city. Our approach provides high prediction accuracy in the real datasets; furthermore, we project significant crime rate reduction using our planning strategy compared to current police strategy.
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process. HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable realworld sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application. Finally, (iv) we show hardness results for the problem that HEALER solves. HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Computational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of security resources. These applications are leading to realworld use-inspired research in the emerging research area of “security games.” The research challenges posed by these applications include scaling up security games to real-world-sized problems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries. In cybersecurity domain, the interaction between the defender and adversary is quite complicated with high degree of incomplete information and uncertainty. While solutions have been proposed for parts of the problem space in cybersecurity, the need of the hour is a comprehensive understanding of the whole space including the interaction with the adversary. We highlight the innovations in security games that could be used to tackle the game problem in cybersecurity.
Aggregating the opinions of different agents is a powerful way to find high-quality solutions to complex problems. However, when using agents in this fashion, there are
two fundamental open questions. First, given a universe of
agents, how to quickly identify which ones should be used
to form a team? Second, given a team of agents, what is the
best way to aggregate their opinions?
Many researchers value diversity when forming teams. LiCalzi and Surucu (2012) and Hong and Page (2004) propose
models where the agents know the utility of the solutions,
and the team converges to the best solution found by one
of its members. Clearly in complex problems the utility of
solutions would not be available, and agents would have to
resort to other methods, such as voting, to take a common
decision. Lamberson and Page (2012) study diversity in the
context of forecasts, where the solutions are represented by
real numbers and the team takes the average of the opinion
of its members. Domains where the possible solutions are
discrete, however, are not captured by such a model.
I proposed a new model to study teams of agents that vote
in discrete solution spaces (Marcolino, Jiang, and Tambe
2013), where I show that a diverse team of weaker agents can
overcome a uniform team made of copies of the best agent.
However, this phenomenon does not always occur, and it is
still necessary to identify when we should use diverse teams
and when uniform teams would be more appropriate.
Hence, in Marcolino et al. (2014b), I shed a new light into
this problem, by presenting a new, more general model of
diversity for teams of voting agents. Using that model I can
predict that diverse teams perform better than uniform teams
in problems with a large action space.
All my predictions are verified in a real system of voting
agents, in the Computer Go domain. I show that: (i) a team
of diverse players gets a higher winning rate than a uniform
team made of copies of the best agent; (ii) the diverse team
plays increasingly better as the board size increases.
Moreover, I also performed an experimental study in the
building design domain. This is a fundamental domain in
the current scenario, since it is known that the design of
a building has a major impact in the consumption of energy throughout its whole lifespan (Lin and Gerber 2014). It
is fundamental to design energy efficient buildings. Meanwhile, it is important to balance other factors, such as construction cost, creating a multi-objective optimization problem. I show that by aggregating the opinions of a team of
agents, a higher number of 1
st ranked solutions in the Pareto
frontier is found than when using a single agent. Moreover,
my approach eliminates falsely reported 1
st ranked solutions
(Marcolino et al. 2014a; 2015).
As mentioned, studying different aggregation rules is also
fundamental. In Jiang et al. (2014), I introduce a novel
method to extract a ranking from agents, based on the frequency that actions are played when sampling them multiple
times. My method leads to significant improvements in the
winning rate in Go games when using the Borda voting rule
to aggregate the generated rankings.
L. S. Marcolino, H. Xu, D. Gerber, B. Kolev, S. Price, E. Pantazis, and M. Tambe. 2015. “Agent Teams for Design Problems .” In International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2015).Abstract
Design imposes a novel social choice problem: using a team
of voting agents, maximize the number of optimal solutions; allowing
a user to then take an aesthetical choice. In an open system of design
agents, team formation is fundamental. We present the first model of
agent teams for design. For maximum applicability, we envision agents
that are queried for a single opinion, and multiple solutions are obtained
by multiple iterations. We show that diverse teams composed of agents
with different preferences maximize the number of optimal solutions,
while uniform teams composed of multiple copies of the best agent are in
general suboptimal. Our experiments study the model in bounded time;
and we also study a real system, where agents vote to design buildings.
Saving energy is a major concern. Hence, it is fundamental to design and construct buildings that are
energy-efficient. It is known that the early stage of architectural design has a significant impact on this matter. However, it is complex to create designs that are
optimally energy efficient, and at the same time balance other essential criterias such as economics, space,
and safety. One state-of-the art approach is to create
parametric designs, and use a genetic algorithm to optimize across different objectives. We further improve
this method, by aggregating the solutions of multiple
agents. We evaluate diverse teams, composed by different agents; and uniform teams, composed by multiple
copies of a single agent. We test our approach across
three design cases of increasing complexity, and show
that the diverse team provides a significantly larger percentage of optimal solutions than single agents.
Stackelberg security games (SSG) have received a significant amount of attention in the literature
for modeling the strategic interactions between a defender and an adversary, in which the defender
has a limited amount of security resources to protect a set of targets from a potential attack by
the adversary. SSGs are at the heart of several significant decision-support applications deployed
in real world security domains. All of these applications rely on standard assumptions made
in SSGs, including that the defender and the adversary each have a single objective which is
to maximize their expected utility. Given the successes and real world impact of previous SSG
research, there is a natural desire to push towards increasingly complex security domains, leading
to a point where considering only a single objective is no longer appropriate.
My thesis focuses on incorporating multiple objectives into SSGs. With multiple conflicting objectives for either the defender or adversary, there is no one solution which maximizes all
objectives simultaneously and tradeoffs between the objectives must be made. Thus, my thesis provides two main contributions by addressing the research challenges raised by considering
SSGs with (1) multiple defender objectives and (2) multiple adversary objectives. These contributions consist of approaches for modeling, calculating, and analyzing the tradeoffs between
objectives in a variety of different settings. First, I consider multiple defender objectives resulting from diverse adversary threats where protecting against each type of threat is treated as a separate objective for the defender. Second, I investigate the defender’s need to balance between the
exploitation of collected data and the exploration of alternative strategies in patrolling domains.
Third, I explore the necessary tradeoff between the efficacy and the efficiency of the defender’s
strategy in screening domains. Forth, I examine multiple adversary objectives for heterogeneous
populations of boundedly rational adversaries that no longer strictly maximize expected utility.
The contributions of my thesis provide the novel game models and algorithmic techniques
required to incorporate multiple objectives into SSGs. My research advances the state of the
art in SSGs and opens up the model to new types of security domains that could not have been
handled previously. As a result, I developed two applications for real world security domains that
either have been or will be tested and evaluated in the field.
. Interdicting the flow of illegal goods (such as drugs and ivory) is a
major security concern for many countries. The massive scale of these networks,
however, forces defenders to make judicious use of their limited resources. While
existing solutions model this problem as a Network Security Game (NSG), they
do not consider humans’ bounded rationality. Previous human behavior modeling
works in Security Games, however, make use of large training datasets that are
unrealistic in real-world situations; the ability to effectively test many models is
constrained by the time-consuming and complex nature of field deployments. In
addition, there is an implicit assumption in these works that a model’s prediction
accuracy strongly correlates with the performance of its corresponding defender
strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In
the following paper, we (1) first demonstrate that predictive reliability is indeed
strong for previous Stackelberg Security Game experiments. We also run our own
set of human subject experiments in such a way that models are restricted to
learning on dataset sizes representative of real-world constraints. In the analysis
on that data, we demonstrate that (2) predictive reliability is extremely weak for
NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set’s exposed attack surface and