2012
Yevgeniy Vorobeychik, Bo An, and Milind Tambe. 2012. “
Adversarial Patrolling Games .” In AAAI Spring Symposium on Security, Sustainability and Health.
AbstractDefender-Attacker Stackelberg games are the foundations of
tools deployed for computing optimal patrolling strategies in
adversarial domains such as the United states Federal Air
Marshals Service and the United States Coast Guard, among
others. In Stackelberg game models of these systems the attacker knows only the probability that each target is covered
by the defender, but is oblivious to the detailed timing of the
coverage schedule. In many real-world situations, however,
the attacker can observe the current location of the defender
and can exploit this knowledge to reason about the defender’s
future moves. We study Stackelberg security games in which
the defender sequentially moves between targets, with moves
constrained by an exogenously specified graph, while the attacker can observe the defender’s current location and his
(stochastic) policy concerning future moves. We offer five
contributions: (1) We model this adversarial patrolling game
(APG) as a stochastic game with special structure and present
several alternative formulations that leverage the general nonlinear programming (NLP) approach for computing equilibria
in zero-sum stochastic games. We show that our formulations
yield significantly better solutions than previous approaches.
(2) We extend the NLP formulation for APG allow for attacks
that may take multiple time steps to unfold. (3) We provide
an approximate MILP formulation that uses discrete defender
move probabilities. (4) We experimentally demonstrate the
efficacy of an NLP-based approach, and systematically study
the impact of network topology on the results. (5) We extend
our model to allow the defender to construct the graph constraining his moves, at some cost, and offer novel algorithms
for this setting, finding that a MILP approximation is much
more effective than the exact NLP in this setting.
2012_3_teamcore_patrolsymp.pdf Yevgeniy Vorobeychik, Bo An, and Milind Tambe. 2012. “
Adversarial Patrolling Games: Extended Abstract .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Short paper).
AbstractDefender-Attacker Stackelberg games are the foundations of tools
deployed for computing optimal patrolling strategies in adversarial domains such as the United states Federal Air Marshals Service
and the United States Coast Guard, among others. In Stackelberg
game models of these systems the attacker knows only the probability that each target is covered by the defender, but is oblivious to
the detailed timing of the coverage schedule. In many real-world
situations, however, the attacker can observe the current location of
the defender and can exploit this knowledge to reason about the
defender’s future moves. We study Stackelberg security games
in which the defender sequentially moves between targets, with
moves constrained by an exogenously specified graph, while the attacker can observe the defender’s current location and his (stochastic) policy concerning future moves.
2012_12_teamcore_patrol_short.pdf Michal Jakob, Zbynek Moler, Antonín Komenday, Zhengyu Yin, Albert Xin Jiang, Matthew P. Johnson, Michal Pechoucek, and Milind Tambe. 2012. “
AgentPolis: Towards a Platform for Fully Agent-based Modeling of Multi-Modal Transportation .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Demonstration Track.
AbstractAgentPolis is a fully agent-based platform for modeling
multi-modal transportation systems. It comprises a highperformance discrete-event simulation core, a cohesive set of
high-level abstractions for building extensible agent-based
models and a library of predefined components frequently
used in transportation models. Together with a suite of supporting tools, AgentPolis enables rapid prototyping and
execution of data-driven simulations of a wide range of mobility and transportation phenomena. We illustrate the capabilities of the platform on a model of fare inspection in
public transportation networks.
2012_22_teamcore_agpolis-aamas-cr-rc2.pdf Jason Tsai, Nicholas Weller, and Milind Tambe. 2012. “
Analysis of Heuristic Techniques for Controlling Contagion .” In AAAI Fall Symposium.
AbstractMany strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing
and peacekeeping operations have both been observed to have
a spreading effect. In this work, we use counterinsurgency as
our illustrative domain. Defined as the effort to block the
spread of support for an insurgency, such operations lack the
manpower to defend the entire population and must focus on
the opinions of a subset of local leaders. As past researchers
of security resource allocation have done, we propose using
game theory to develop such policies and model the interconnected network of leaders as a graph.
Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address
this new class of security games, recent research has used
novel heuristic oracles in a double oracle formulation to generate mixed strategies. However, these heuristic oracles were
evaluated only on runtime and quality scaling with the graph
size. Given the complexity of the problem, numerous other
problem features and metrics must be considered to better
inform practical application of such techniques. Thus, this
work provides a thorough experimental analysis including
variations of the contagion probability average and standard
deviation. We extend the previous analysis to also examine
the size of the action set constructed in the algorithms and the
final mixed strategies themselves. Our results indicate that
game instances featuring smaller graphs and low contagion
probabilities converge slowly while games with larger graphs
and medium contagion probabilities converge most quickly.
2012_40_teamcore_fall_symposium_-_final_submission.pdf Matthew P. Johnson, Fei Fang, Rong Yang, Milind Tambe, and Heidi Jo Albers. 2012. “
Challenges in Patrolling to Maximize Pristine Forest Area (Position Paper) .” In AAAI Spring Symposium on Game Theory for Security, Sustainability and Health.
AbstractIllegal extraction of forest resources is fought, in many
developing countries, by patrols through the forest that
seek to deter such activity by decreasing its profitability.
With limited resources for performing such patrols, a patrol strategy will seek to distribute the patrols throughout
the forest, in space and time, in order to minimize the resulting amount of extraction that occurs or maximize the
degree of forest protection, according to one of several
potential metrics. We pose this problem as a Stackelberg
game. We adopt and extend the simple, geometrically elegant model of (Robinson 2010). First, we study optimal
allocations of patrol density under generalizations of this
model, relaxing several of its assumptions. Second, we
pose the problem of generating actual schedules whose
site visit frequencies are consistent with the analytically
computed optimal patrol densities.
2012_5_teamcore_forest.pdf Rong Yang, Fernando Ordonez, and Milind Tambe. 2012. “
Computing Optimal Strategy against Quantal Response in Security Games .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractTo step beyond the first-generation deployments of attacker-defender
security games – for LAX Police, US FAMS and others – it is critical that we relax the assumption of perfect rationality of the human
adversary. Indeed, this assumption is a well-accepted limitation of
classical game theory and modeling human adversaries’ bounded
rationality is critical. To this end, quantal response (QR) has provided very promising results to model human bounded rationality.
However, in computing optimal defender strategies in real-world
security games against a QR model of attackers, we face difficulties
including (1) solving a nonlinear non-convex optimization problem
efficiently for massive real-world security games; and (2) addressing constraints on assigning security resources, which adds to the
complexity of computing the optimal defender strategy.
This paper presents two new algorithms to address these difficulties: GOSAQ can compute the globally optimal defender strategy against a QR model of attackers when there are no resource
constraints and gives an efficient heuristic otherwise; PASAQ in
turn provides an efficient approximation of the optimal defender strategy with or without resource constraints. These two novel algorithms are based on three key ideas: (i) use of a binary
search method to solve the fractional optimization problem efficiently, (ii) construction of a convex optimization problem through
a non-linear transformation, (iii) building a piecewise linear approximation of the non-linear terms in the problem. Additional
contributions of this paper include proofs of approximation bounds, detailed experimental results showing the advantages of GOSAQ
and PASAQ in solution quality over the benchmark algorithm (BRQR)
and the efficiency of PASAQ. Given these results, PASAQ is at the
heart of the PROTECT system, which is deployed for the US Coast
Guard in the port of Boston, and is now headed to other ports.
2012_42_teamcore_aamas2012_paper_cameraready.pdf Laura Klein, Jun-young Kwak, Geoffrey Kavulya, Farrokh Jazizadeh, Burcin Becerik-Gerber, Pradeep Varakantham, and Milind Tambe. 2012. “
Coordinating Occupant Behavior for Building Energy and Comfort Management using Multi-Agent Systems .” Automation in Construction: An International Research Journal, 22, Pp. 525-536.
AbstractThere is growing interest in reducing building energy consumption through increased sensor data and increased computational support for building controls. The goal of reduced building energy is often coupled with the desire for improved occupant comfort. Current building systems are inefficient in their energy usage for maintaining occupant comfort as they operate according to fixed schedules and maximum design occupancy assumptions, and they rely on code defined occupant comfort ranges. This paper presents and implements a multi-agent comfort and energy system (MACES) to model alternative management and control of building systems and occupants. MACES specifically improves upon previous multi-agent systems as it coordinates both building system devices and building occupants through direct changes to occupant meeting schedules using multi-objective Markov Decision Problems (MDP). MACES is implemented and tested with input from a real-world building including actual thermal zones, temperatures, occupant preferences, and occupant schedules. The operations of this building are then simulated according to three distinct control strategies involving varying levels of intelligent coordination of devices and occupants. Finally, the energy and comfort results of these three strategies are compared to the baseline and opportunities for further energy savings are assessed. A 12% reduction in energy consumption and a 5% improvement in occupant comfort are realized as compared to the baseline control. Specifically, by employing MDP meeting relocating, an additional 5% improvement in energy consumption is realized over other control strategies.
2012. “
Deployed Security Games for Patrol Planning .” In Handbook on Operations Research for Homeland Security.
AbstractNations and organizations need to secure locations of economic, military,
or political importance from groups or individuals that can cause harm. The fact
that there are limited security resources prevents complete security coverage, which
allows adversaries to observe and exploit patterns in patrolling or monitoring, and
enables them to plan attacks that avoid existing patrols. The use of randomized security policies that are more difficult for adversaries to predict and exploit can counter
their surveillance capabilities and improve security. In this chapter we describe the
recent development of models to assist security forces in randomizing their patrols
and their deployment in real applications.
The systems deployed are based on fast algorithms for solving large instances of
Bayesian Stackelberg games that capture the interaction between security forces and
adversaries. Here we describe a generic mathematical formulation of these models,
present some of the results that have allowed these systems to be deployed in practice, and outline remaining future challenges. We discuss the deployment of these
systems in two real-world security applications: 1) The police at the Los Angeles
International Airport uses these models to randomize the placement of checkpoints
on roads entering the airport and the routes of canine unit patrols within the airport
terminals. 2) The Federal Air Marshal Service uses these models to randomize the
schedules of air marshals on international flights.
2012_2_teamcore_patrollingchapter.pdfManish Jain, Kevin Leyton-Brown, and Milind Tambe. 2012. “
The Deployment-to-Saturation Ratio in Security Games .” In Conference on Artificial Intelligence (AAAI).
AbstractStackelberg security games form the backbone of systems like
ARMOR, IRIS and PROTECT, which are in regular use by the
Los Angeles International Police, US Federal Air Marshal Service and the US Coast Guard respectively. An understanding
of the runtime required by algorithms that power such systems
is critical to furthering the application of game theory to other
real-world domains. This paper identifies the concept of the
deployment-to-saturation ratio in random Stackelberg security
games, and shows that problem instances for which this ratio
is 0.5 are computationally harder than instances with other
deployment-to-saturation ratios for a wide range of different
equilibrium computation methods, including (i) previously
published different MIP algorithms, and (ii) different underlying solvers and solution mechanisms. This finding has at
least two important implications. First, it is important for new
algorithms to be evaluated on the hardest problem instances.
We show that this has often not been done in the past, and
introduce a publicly available benchmark suite to facilitate
such comparisons. Second, we provide evidence that this computationally hard region is also one where optimization would
be of most benefit to security agencies, and thus requires significant attention from researchers in this area. Furthermore,
we use the concept of phase transitions to better understand
this computationally hard region. We define a decision problem related to security games, and show that the probability
that this problem has a solution exhibits a phase transition
as the deployment-to-saturation ratio crosses 0.5. We also
demonstrate that this phase transition is invariant to changes
both in the domain and the domain representation, and that
the phase transition point corresponds to the computationally
hardest instances.
2012_28_teamcore_jain_finalsubmission.pdf Rong Yang, Fei Fang, Albert Xin Jiang, Karthik Rajagopal, Milind Tambe, and Rajiv Maheswaran. 2012. “
Designing Better Strategies against Human Adversaries in Network Security Games: Extended Abstract .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS)(Short paper) .
AbstractIn a Network Security Game (NSG), security agencies must allocate limited resources to protect targets embedded in a network,
such as important buildings in a city road network. A recent line
of work relaxed the perfect-rationality assumption of human adversary and showed significant advantages of incorporating the bounded rationality adversary models in non-networked security domains. Given that real-world NSG are often extremely complex and
hence very difficult for humans to solve, it is critical that we address
human bounded rationality when designing defender strategies. To
that end, the key contributions of this paper include: (i) comprehensive experiments with human subjects using a web-based game
that we designed to simulate NSGs; (ii) new behavioral models of
human adversary in NSGs, which we train with the data collected
from human experiments; (iii) new algorithms for computing the
defender optimal strategy against the new models.
2012_15_teamcore_aamas2012_gsg.pdf Matthew P Johnson, Fei Fang, Milind Tambe, and H. J. Albers. 2012. “
Designing Patrol Strategies to Maximize Pristine Forest Area .” In Workshop on Optimization in Multiagent Systems (OPTMAS) at AAMAS .
AbstractIllegal extraction of forest resources is fought, in many developing
countries, by patrols that seek to deter such activity by decreasing its profitability. With a limited budget, a patrol strategy will
seek to distribute the patrols throughout the forest, in order to minimize the resulting amount of extraction that occurs or maximize the
amount of “pristine” forest area. Prior work in forest economics
has posed this problem as a Stackelberg game, but efficient optimal or approximation algorithms for generating leader strategies
have not previously been found. Unlike previous work on Stackelberg games in the multiagent literature, much of it motivated by
counter-terrorism, here we seek to protect a continuous area, as
much as possible, from extraction by an indeterminate number of
followers. The continuous nature of this problem setting leads to
new challenges and solutions, very different in character from in
the discrete Stackelberg settings previously studied.
In this paper, we give an optimal patrol allocation algorithm and a
guaranteed approximation algorithm, the latter of which is more efficient and yields simpler, more practical patrol allocations. In our
experimental investigations, we find that these algorithms perform significantly better—yielding a larger pristine area—than naive
patrol allocations.
2012_34_teamcore_forestoptmas2012_cameraready.pdf Bostjan Kaluza, Gal Kaminka, and Milind Tambe. 2012. “
Detection of Suspicious Behavior from a Sparse Set of Multiagent Interactions .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractIn many multiagent domains, no single observation event is sufficient to determine that the behavior of individuals is suspicious. Instead, suspiciousness must be inferred from a combination of multiple events, where events refer to the individual’s interactions with
other individuals. Hence, a detection system must employ a detector that combines evidence from multiple events, in contrast to most
previous work, which focuses on the detection of a single, clearly
suspicious event. This paper proposes a two-step detection system,
where it first detects trigger events from multiagent interactions,
and then combines the evidence to provide a degree of suspicion.
The paper provides three key contributions: (i) proposes a novel
detector that generalizes a utility-based plan recognition with arbitrary utility functions, (ii) specifies conditions that any reasonable
detector should satisfy, and (iii) analyzes three detectors and compares them with the proposed approach. The results on a simulated
airport domain and a dangerous-driver domain show that our new
algorithm outperforms other approaches in several settings.
2012_18_teamcore_paper-aamas2012.cr_.pdf Jason Tsai, Emma Bowring, Stacy Marsella, Wendy Wood, and Milind Tambe. 2012. “
Emotional Contagion with Virtual Characters: Extended Abstract .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS)(Short paper).
AbstractIn social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [8]. While it has been observed in humanhuman interactions, no known studies have examined its existence
in agent-human interactions. As virtual characters make their way
into high-risk, high-impact applications such as psychotherapy and
military training with increasing frequency, the emotional impact
of the agents’ expressions must be accurately understood to avoid
undesirable repercussions.
2012_16_teamcore_ec-shortpaper.pdf Milind Tambe and Bo An. 2012. “
Game Theory for Security: A Real-World Challenge Problem for Multiagent Systems and Beyond .” In AAAI Spring Symposium on Game Theory for Security, Sustainability and Health.
AbstractThe goal of this paper is to introduce a real-world challenge
problem for researchers in multiagent systems and beyond,
where our collective efforts may have a significant impact
on activities in the real-world. The challenge is in applying
game theory for security: Our goal is not only to introduce
the problem, but also to provide exemplars of initial successes of deployed systems in this challenge problem arena, some
key open research challenges and pointers to getting started
in this research.
2012_4_teamcore_aaaiss12challenge.pdf Bo An and Milind Tambe. 2012. “
Game Theory for Security: An Important Challenge for Multiagent Systems .” In European Workshop on Multiagent Systems (EUMAS) 2011 workshop (Invited) .
AbstractThe goal of this paper is to introduce a real-world challenge problem
for researchers in multiagent systems and beyond, where our collective efforts
may have a significant impact on activities in the real-world. The challenge is in
applying game theory for security: Our goal is not only to introduce the problem, but also to provide exemplars of initial successes of deployed systems in
this challenge problem arena, some key open research challenges and pointers to
getting started in this research.
2012_24_teamcore_eumas.pdf Milind Tambe, Manish Jain, James Adam Pita, and Albert Xin Jiang. 2012. “
Game Theory for Security: Key Algorithmic Principles, Deployed Systems, Lessons Learned.” In 50th Annual Allerton Conference on Communication, Control, and Computing .
AbstractSecurity is a critical concern around the world. In
many security domains, limited security resources prevent full
security coverage at all times; instead, these limited resources
must be scheduled, avoiding schedule predictability, while
simultaneously taking into account different target priorities,
the responses of the adversaries to the security posture and
potential uncertainty over adversary types.
Computational game theory can help design such unpredictable security schedules. Indeed, casting the problem as a
Bayesian Stackelberg game, we have developed new algorithms
that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to
real-world use-inspired research in the emerging research area
of “security games”; specifically, the research challenges posed
by these applications include scaling up security games to largescale problems, handling significant adversarial uncertainty,
dealing with bounded rationality of human adversaries, and
other interdisciplinary challenges.
2012_43_teamcore_allerton.pdf Ondrej Vanek, Zhengyu Yin, Manish Jain, Branislav Bosansky, Milind Tambe, and Michal Pechoucek. 2012. “
Game-theoretic Resource Allocation for Malicious Packet Detection in Computer Networks .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractWe study the problem of optimal resource allocation for
packet selection and inspection to detect potential threats in
large computer networks with multiple valuable computers
of differing importance. An attacker tries to harm these targets by sending malicious packets from multiple entry points
of the network; the defender thus needs to optimally allocate his resources to maximize the probability of malicious
packet detection under network latency constraints.
We formulate the problem as a graph-based security game
with multiple resources of heterogeneous capabilities and
propose a mathematical program for finding optimal solutions. Due to the very limited scalability caused by the large
attacker’s strategy space and non-linearity of the program,
we investigate solutions with approximated utility function
and propose Grande, a novel polynomial approximate algorithm utilizing submodularity of the problem able to find
solutions with a bounded error on problem of a realistic size.
2012_14_teamcore_gt-approach-to-net-sec.pdf Jason Tsai, Thanh H. Nguyen, and Milind Tambe. 2012. “
Game-Theoretic Target Selection in Contagion-based Domains .” In Workshop on Optimization in Multiagent Systems (OPTMAS) at AAMAS .
AbstractMany strategic actions carry a ‘contagious’ component beyond the
immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading
effect. In this work, we use counterinsurgency as our illustrative
domain. Defined as the effort to block the spread of support for an
insurgency, such operations lack the manpower to defend the entire
population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done,
we propose using game theory to develop such policies and model
the interconnected network of leaders as a graph.
Unlike this past work in security games, actions in these domains
possess a probabilistic, non-local impact. To address this new class
of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel
heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks,
and scale-free graphs. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by
our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected scale-free graphs.
2012_33_teamcore_infblock2.pdf James Pita. 2012. “
The Human Element: Addressing Human Adversaries in Security Domains ”.
AbstractRecently, game theory has been shown to be useful for reasoning about real-world security settings where security forces must protect critical assets from potential adversaries. In fact, there
have been a number of deployed real-world applications of game theory for security (e.g., ARMOR at Los Angeles International Airport and IRIS for the Federal Air Marshals Service). Here,
the objective is for the security force to utilize its limited resources to best defend their critical
assets.
An important factor in these real-world security settings is that the adversaries involved are
humans who may not behave according to the standard assumptions of game-theoretic models.
There are two key shortcomings of the approaches currently employed in these recent applications. First, human adversaries may not make the predicted rational decision. In such situations,
where the security force has optimized against a perfectly rational opponent, a deviation by the
human adversary can lead to adverse affects on the security force’s predicted outcome. Second,
human adversaries are naturally creative and security domains are highly dynamic, making enumeration of all potential threats a practically impossible task and solving the resulting game, with
current leading approaches, would be intractable.
My thesis contributes to a very new area that combines algorithmic and experimental gametheory. Indeed, it examines a critical problem in applying game-theoretic techniques to situations where perfectly rational solvers must address human adversaries. In doing so it advances the
study and reach of game theory to domains where software agents and humans may interact.
More specifically, to address the first shortcoming, my thesis presents two separate algorithms
to address potential deviations from the predicted rational decision by human adversaries. Experimental results, from a simulation that is motivated by a real-world security domain at Los
Angeles International airport, demonstrated that both of my approaches outperform the currently
deployed optimal algorithms which utilize standard game-theoretic assumptions and additional
alternative algorithms against humans. In fact, one of my approaches is currently under evaluation in a real-world application to aid in resource allocation decisions for the United States Coast
Guard.
Towards addressing the second shortcoming of enumeration of a large number of potential
adversary threat capabilities, I introduce a new game-theoretic model for efficiency, which additionally generalizes the previously accepted model for security domains. This new game-theoretic
model for addressing human threat capabilities has seen real-world deployment and is under evaluation to aid the United States Transportation Security Administration in their resource allocation
challenges.
2012_44_teamcore_james_phd_thesis.pdf Farrokh Jazizadeha, Geoffrey Kavulyaa, Jun-young Kwak, Burcin Becerik-Gerber, Milind Tambe, and Wendy Wood. 2012. “
Human-Building Interaction for Energy Conservation in Office Buildings.” In Construction Research Congress .
AbstractBuildings are one of the major consumers of energy in the U.S. Both commercial and
residential buildings account for about 42% of the national U.S. energy consumption.
The majority of commercial buildings energy consumption is attributed to lighting
(25%), space heating and cooling (25%), and ventilation (7%). Several research
studies and industrial developments have focused on energy management based on
maximum occupancy. However, fewer studies, with the objective of energy savings,
have considered human preferences. This research focuses on office buildings’
occupants’ preferences and their contribution to the building energy conservation.
Accordingly, occupants of selected university campus offices were asked to reduce
lighting levels in their offices during work hours. Different types of information
regarding their energy consumption were provided to the occupants. Email messages
were used to communicate with the occupants. To monitor behavioral changes during
the study, the test bed offices were equipped with wireless light sensors. The
deployed light sensors were capable of detecting variations in light intensity, which
was correlated with energy consumption. The impact of different types of information
on occupant’s energy related behavior is presented.
2012_36_teamcore_crc_final_paper.pdf