2013
Thanh H. Nguyen, Amos Azaria, James Pita, Rajiv Maheswaran, Sarit Kraus, and Milind Tambe. 2013. “
Modeling Human Adversary Decision Making in Security Games: An Initial Report (Extended Abstract) .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) [SHORT PAPER].
AbstractMotivated by recent deployments of Stackelberg security games
(SSGs), two competing approaches have emerged which either integrate models of human decision making into game-theoretic algorithms or apply robust optimization techniques that avoid adversary
modeling. Recently, a robust technique (MATCH) has been shown
to significantly outperform the leading modeling-based algorithms
(e.g., Quantal Response (QR)) even in the presence of significant
amounts of subject data. As a result, the effectiveness of using human behaviors in solving SSGs remains in question. We study this
question in this paper.
2013_9_teamcore_ext103-nguyen.pdf Albert Xin Jiang, Thanh H. Nguyen, Milind Tambe, and Ariel D. Procaccia. 2013. “
Monotonic Maximin: A Robust Stackelberg Solution Against Boundedly Rational Followers .” In Conference on Decision and Game Theory for Security (GameSec).
AbstractThere has been recent interest in applying Stackelberg games to infrastructure security, in which a defender must protect targets from attack by an
adaptive adversary. In real-world security settings the adversaries are humans
and are thus boundedly rational. Most existing approaches for computing defender strategies against boundedly rational adversaries try to optimize against
specific behavioral models of adversaries, and provide no quality guarantee when
the estimated model is inaccurate. We propose a new solution concept, monotonic maximin, which provides guarantees against all adversary behavior models
satisfying monotonicity, including all in the family of Regular Quantal Response
functions. We propose a mixed-integer linear program formulation for computing
monotonic maximin. We also consider top-monotonic maximin, a related solution
concept that is more conservative, and propose a polynomial-time algorithm for
top-monotonic maximin.
2013_31_teamcore_robustqr.pdf Leandro Soriano Marcolino, Albert Xin Jiang, and Milind Tambe. 2013. “
Multi-agent Team Formation: Diversity Beats Strength? .” In International Joint Conference on Artificial Intelligence (IJCAI).
AbstractTeam formation is a critical step in deploying a
multi-agent team. In some scenarios, agents coordinate by voting continuously. When forming such
teams, should we focus on the diversity of the team
or on the strength of each member? Can a team
of diverse (and weak) agents outperform a uniform
team of strong agents? We propose a new model
to address these questions. Our key contributions
include: (i) we show that a diverse team can overcome a uniform team and we give the necessary
conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that
both diversity and strength contribute to the performance of a team; (iv) we show experiments that
demonstrate the usefulness of our model in one of
the most difficult challenges for Artificial Intelligence: Computer Go.
2013_18_teamcore_ijcai13.pdf Fei Fang, Albert Xin Jiang, and Milind Tambe. 2013. “
Optimal Patrol Strategy for Protecting Moving Targets with Multiple Mobile Resources .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractPrevious work on Stackelberg Security Games for scheduling security resources has mostly assumed that the targets are stationary
relative to the defender and the attacker, leading to discrete game
models with finite numbers of pure strategies. This paper in contrast focuses on protecting mobile targets that lead to a continuous
set of strategies for the players. The problem is motivated by several real-world domains including protecting ferries with escorts and
protecting refugee supply lines. Our contributions include: (i) a
new game model for multiple mobile defender resources and moving targets with a discretized strategy space for the defender and a
continuous strategy space for the attacker; (ii) an efficient linearprogram-based solution that uses a compact representation for the
defender’s mixed strategy, while accurately modeling the attacker’s
continuous strategy using a novel sub-interval analysis method; (iii)
a heuristic method of equilibrium refinement for improved robustness and (iv) detailed experimental analysis in the ferry protection
domain.
2013_8_teamcore_fp052-fang.pdf Francesco Maria Delle Fave, Yundi Qian, Albert Xin Jiang, Matthew Brown, and Milind Tambe. 2013. “
Planning and Learning in Security Games .” In ACM SigEcom Exchanges, 3rd ed. Vol. 11.
AbstractWe present two new critical domains where security games are applied to generate randomized
patrol schedules. For each setting, we present the current research that we have produced. We
then propose two new challenges to build accurate schedules that can be deployed effectively in
the real world. The first is a planning challenge. Current schedules cannot handle interruptions.
Thus, more expressive models, that allow for reasoning over stochastic actions, are needed. The
second is a learning challenge. In several security domains, data can be used to extract information
about both the environment and the attacker. This information can then be used to improve the
defender’s strategies.
2013_22_teamcore_sigecom2013.pdf Nan Li, Jun-young Kwak, Burcin Becerik-Gerber, and Milind Tambe. 2013. “
Predicting HVAC Energy Consumption in Commercial Buildings using Multiagent Systems .” In International Symposium on Automation and Robotics in Construction (ISARC).
AbstractEnergy consumption in commercial buildings has been increasing rapidly in the past decade. The
knowledge of future energy consumption can bring significant value to commercial building energy
management. For example, prediction of energy consumption decomposition helps analyze the energy
consumption patterns and efficiencies as well as waste, and identify the prime targets for energy
conservation. Moreover, prediction of temporal energy consumption enables building managers to plan out
the energy usage over time, shift energy usage to off-peak periods, and make more effective energy
purchase plans. This paper proposes a novel model for predicting heating, ventilation and air conditioning
(HVAC) energy consumption in commercial buildings. The model simulates energy behaviors of HVAC
systems in commercial buildings, and interacts with a multiagent systems (MAS) based framework for
energy consumption prediction. Prediction is done on a daily, weekly and monthly basis. Ground truth
energy consumption data is collected from a test bed office building over 267 consecutive days, and is
compared to predicted energy consumption for the same period. Results show that the prediction can match
92.6 to 98.2% of total HVAC energy consumption with coefficient of variation of the root mean square
error (CV-RMSE) values of 7.8 to 22.2%. Ventilation energy consumption can be predicted at high
accuracies (over 99%) and low variations (CV-RMSE values of 3.1 to 16.3%), while cooling energy
consumption accounts for majority of inaccuracies and variations in total energy consumption prediction.
2013_23_teamcore_camera_ready_predicting_hvac_energy_consumption_in_commercial_buildings_using_multiagent_systems.pdf Fei Fang, Albert Xin Jiang, and Milind Tambe. 2013. “
Protecting Moving Targets with Multiple Mobile Resources .” Journal of Artificial Intelligence Research, 48, Pp. 583-634.
AbstractIn recent years, Stackelberg Security Games have been successfully applied to solve resource
allocation and scheduling problems in several security domains. However, previous work has mostly assumed that the targets are stationary relative to the defender and the attacker, leading to discrete
game models with finite numbers of pure strategies. This paper in contrast focuses on protecting
mobile targets that leads to a continuous set of strategies for the players. The problem is motivated
by several real-world domains including protecting ferries with escort boats and protecting refugee
supply lines. Our contributions include: (i) A new game model for multiple mobile defender resources and moving targets with a discretized strategy space for the defender and a continuous
strategy space for the attacker. (ii) An efficient linear-programming-based solution that uses a
compact representation for the defender’s mixed strategy, while accurately modeling the attacker’s
continuous strategy using a novel sub-interval analysis method. (iii) Discussion and analysis of
multiple heuristic methods for equilibrium refinement to improve robustness of defender’s mixed
strategy. (iv) Discussion of approaches to sample actual defender schedules from the defender’s
mixed strategy. (iv) Detailed experimental analysis of our algorithms in the ferry protection domain.
2013_34_teamcore_jair_ferry.pdf Jason Tsai. 2013. “
Protecting Networks Against Diffusive Attacks: Game-Theoretic Resource Allocation for Contagion Mitigation ”.
AbstractMany real-world situations involve attempts to spread influence through a social network. For
example, viral marketing is when a marketer selects a few people to receive some initial advertisement in the hopes that these ‘seeds’ will spread the news. Even peacekeeping operations
in one area have been shown to have a contagious effect on the neighboring vicinity. Each of
these domains also features multiple parties seeking to maximize or mitigate a contagious effect
by spreading its own influence among a select few seeds, naturally yielding an adversarial resource allocation problem. My work models the interconnected network of people as a graph and
develops algorithms to optimize resource allocation in these networked competitive contagion
scenarios.
Game-theoretic resource allocation in the past has not considered domains with both a networked structure and contagion effects, rendering them unusable in critical domains such as rumor control, counterinsurgency, and crowd management. Networked domains without contagion
effects already present computational challenges due to the large scale of the action space. To
address this issue, my first contribution proposed efficient game-theoretic allocation algorithms
for the graph-based urban road network domain. This work still provides the only polynomialtime algorithm for allocating vehicle checkpoints through a city, giving law enforcement officers
an efficient tool to combat terrorists making their way to potential points of attack. Second, I have provided the first game-theoretic treatment for contagion mitigation in social networks and
given practitioners the first principled techniques for such vital concerns as rumor control and
counterinsurgency. Finally, I extended my work on game-theoretic contagion mitigation to address uncertainty about the network structure to find that, contrary to what evidence and intuition
suggest, heuristic sampling approaches provide near-optimal solutions across a wide range of
generative graph models and uncertainty models. Thus, despite extreme practical challenges in
attaining accurate social network information, my techniques remain near-optimal across numerous forms of uncertainty in multiple synthetic and real-world graph structures.
Beyond optimization of resource allocation, I have further studied contagion effects to understand the effectiveness of such resources. First, I created an evacuation simulation, ESCAPES,
to explore the interaction of pedestrian fear contagion and authority fear mitigation during an
evacuation. Second, using this simulator, I have advanced the frontier in contagion modeling
by developing empirical evaluation methods for comparing and calibrating computational contagion models that are critical in crowd simulations and evacuation modeling. Finally, I have
also conducted an examination of agent-human emotional contagion to inform the rising use of
simulations for personnel training in emotionally-charged situations.
2013_24_teamcore_jasontsai-dissertation.pdf Nupul Kukreja, William G. J. Halfond, and Milind Tambe. 2013. “
Randomizing Regression Tests using Game Theory .” In International conference on Automated Software Engineering (ASE).
AbstractAs software evolves, the number of test-cases in the
regression test suites continues to increase, requiring testers to
prioritize their execution. Usually only a subset of the test cases
is executed due to limited testing resources. This subset is often
known to the developers who may try to “game” the system by
committing insufficiently tested code for parts of the software
that will not be tested. In this new ideas paper, we propose a
novel approach for randomizing regression test scheduling, based
on Stackelberg games for deployment of scarce resources. We
apply this approach to randomizing test cases in such a way
as to maximize the testers’ expected payoff when executing the
test cases. Our approach accounts for resource limitations (e.g.,
number of testers) and provides a probabilistic distribution for
scheduling test cases. We provide an example application of our
approach showcasing the idea of using Stackelberg games for
randomized regression test scheduling.
2013_32_teamcore_ase.pdf Rong Yang, Albert Xin Jiang, Milind Tambe, and Fernando Ordo´nez. 2013. “
Scaling-up Security Games with Boundedly Rational Adversaries: A Cutting-plane Approach .” In International Joint Conference on Artificial Intelligence (IJCAI).
AbstractTo improve the current real-world deployments of
Stackelberg security games (SSGs), it is critical
now to efficiently incorporate models of adversary
bounded rationality in large-scale SSGs. Unfortunately, previously proposed branch-and-price approaches fail to scale-up given the non-convexity of
such models, as we show with a realization called
COCOMO. Therefore, we next present a novel
cutting-plane algorithm called BLADE to scale-up
SSGs with complex adversary models,with three
key novelties: (i) an efficient scalable separation oracle to generate deep cuts; (ii) a heuristic that uses
gradient to further improve the cuts; (iii) techniques
for quality-efficiency tradeoff.
2013_19_teamcore_ijcai13_yang.pdf Jason Tsai, Yundi Qian, Yevgeniy Vorobeychik, Christopher Kiekintveld, and Milind Tambe. 2013. “
Security Games with Contagion: Handling Asymmetric Information .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) [SHORT PAPER].
AbstractCounterinsurgency, which is the effort to mitigate support for an
opposing organization, is one such domain that has been studied
recently and past work has modeled the problem as an influence
blocking maximization that features an influencer and a mitigator.
While past work has introduced scalable heuristic techniques for
generating effective strategies using a double oracle algorithm, it
has not addressed the issue of uncertainty and asymmetric information, which is the topic of this paper.
2013_3_teamcore_aamas13_camera_readyv3.pdf Bo An, Matthew Brown, Yevgeniy Vorobeychik, and Milind Tambe. 2013. “
Security Games with Surveillance Cost and Optimal Timing of Attack Execution.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractStackelberg games have been used in several deployed applications
to allocate limited resources for critical infrastructure protection.
These resource allocation strategies are randomized to prevent a
strategic attacker from using surveillance to learn and exploit patterns in the allocation. Past work has typically assumed that the
attacker has perfect knowledge of the defender’s randomized strategy or can learn the defender’s strategy after conducting a fixed
period of surveillance. In consideration of surveillance cost, these
assumptions are clearly simplistic since attackers may act with partial knowledge of the defender’s strategies and may dynamically
decide whether to attack or conduct more surveillance. In this
paper, we propose a natural model of limited surveillance in which
the attacker dynamically determine a place to stop surveillance in
consideration of his updated belief based on observed actions and
surveillance cost. We show an upper bound on the maximum number of observations the attacker can make and show that the attacker’s optimal stopping problem can be formulated as a finite state
space MDP. We give mathematical programs to compute optimal
attacker and defender strategies. We compare our approaches with
the best known previous solutions and experimental results show
that the defender can achieve significant improvement in expected
utility by taking the attacker’s optimal stopping decision into account, validating the motivation of our work.
2013_2_teamcore_sglsc.pdf Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Yu-Han Chang, Milind Tambe, Burcin Becerik-Gerber, and Wendy Wood. 2013. “
TESLA: An Energy-saving Agent that Leverages Schedule Flexibility .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractThis innovative application paper presents TESLA, an agent-based
application for optimizing the energy use in commercial buildings.
TESLA’s key insight is that adding flexibility to event/meeting
schedules can lead to significant energy savings. TESLA provides three key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energyefficient scheduling of incrementally/dynamically arriving meetings and events; (ii) an algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility; and (iii) surveys of real users that indicate that TESLA’s
assumptions exist in practice. TESLA was evaluated on data of
over 110,000 meetings held at nine campus buildings during eight
months in 2011–2012 at USC and SMU. These results show that,
compared to the current systems, TESLA can substantially reduce
overall energy consumption.
2013_4_teamcore_aamas13-tesla-camera-ready-finalv2.pdf Manish Jain. 2013. “
Thwarting Adversaries with Unpredictability: Massive-scale Game-Theoretic Algorithms for Real-world Security Deployments ”.
AbstractProtecting critical infrastructure and targets such as airports, transportation networks, power
generation facilities as well as critical natural resources and endangered species is an important
task for police and security agencies worldwide. Securing such potential targets using limited
resources against intelligent adversaries in the presence of the uncertainty and complexities of
the real-world is a major challenge. My research uses a game-theoretic framework to model the
strategic interaction between a defender (or security forces) and an attacker (or terrorist adversary)
in security domains.
Game theory provides a sound mathematical approach for deploying limited security resources
to maximize their effectiveness. While game theory has always been popular in the arena of
security, unfortunately, the state of the art algorithms either fail to scale or to provide a correct
solution for large problems with arbitrary scheduling constraints. For example, US carriers fly over
27,000 domestic and 2,000 international flights daily, presenting a massive scheduling challenge
for Federal Air Marshal Service (FAMS).
My thesis contributes to a very new area that solves game-theoretic problems using insights
from large-scale optimization literature towards addressing the computational challenge posed by
real-world domains. I have developed new models and algorithms that compute optimal strategies
for scheduling defender resources is large real-world domains. My thesis makes the following contributions. First, it presents new algorithms that can solve for trillions of actions for both
the defender and the attacker. Second, it presents a hierarchical framework that provides orders
of magnitude scale-up in attacker types for Bayesian Stackelberg games. Third, it provides an
analysis and detection of a phase-transition that identifies properties that makes security games
hard to solve.
These new models have not only advanced the state of the art in computational game-theory,
but have actually been successfully deployed in the real-world. My work represents a successful
transition from game-theoretic advancements to real-world applications that are already in use, and
it has opened exciting new avenues to greatly expand the reach of game theory. For instance, my
algorithms are used in the IRIS system: IRIS has been in use by the Federal Air Marshals Service
(FAMS) to schedule air marshals on board international commercial flights since October 2009.
2013_25_teamcore_manish_thesis.pdf Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Yu-Han Chang, Milind Tambe, Burcin Becerik-Gerber, and Wendy Wood. 2013. “
Why TESLA Works: Innovative Agent-based Application Leveraging Schedule Flexibility for Conserving Energy .” In Workshop on Multiagent-based Societal Systems (MASS) at AAMAS.
AbstractThis paper presents TESLA, an agent-based application for optimizing the energy use in commercial buildings. TESLA’s key insight is that adding flexibility to event/meeting schedules can lead
to significant energy savings. TESLA provides two key contributions: (i) three online scheduling algorithms that consider flexibility of people’s preferences for energy-efficient scheduling of incrementally/dynamically arriving meetings and events; and (ii) an
algorithm to effectively identify key meetings that lead to significant energy savings by adjusting their flexibility. TESLA was
evaluated on data of over 110,000 meetings held at nine campus
buildings during eight months in 2011–2012 at the University of
Southern California (USC) and the Singapore Management University (SMU), and it indicated that TESLA’s assumptions exist in
practice. This paper also provides an extensive analysis on energy
savings achieved by TESLA. These results and analysis show that,
compared to the current systems, TESLA can substantially reduce
overall energy consumption.
2013_12_teamcore_mass-camera-ready-v2.pdf Manish Jain, Vincent Conitzer, and Milind Tambe. 2013. “
Security Scheduling for Real-world Networks .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractNetwork based security games, where a defender strategically places security measures on the edges of a graph to protect against an adversary, who chooses a path through a graph is an important research problem with potential for real-world impact. For example, police forces face the problem of placing checkpoints on roads to inspect vehicular traffic in their day-to-day operations, a security measure the Mumbai police have performed since the terrorist attacks in 2008. Algorithms for solving such network-based security problems have been proposed in the literature, but none of them scale up to solving problems of the size of real-world networks. In this paper, we present SNARES, a novel algorithm that computes optimal solutions for both the defender and the attacker in such network security problems. Based on a double-oracle framework, SNARES makes novel use of two approaches: warm starts and greedy responses. It makes the following contributions: (1) It defines and uses mincut-fanout, a novel method for efficient warm-starting of the computation; (2) It exploits the submodularity property of the defender optimization in a greedy heuristic, which is used to generate “better-responses"; SNARES also uses a better-response computation for the attacker. Furthermore, we evaluate the performance of SNARES in real-world networks illustrating a significant advance: whereas state-of-the-art algorithms could handle just the southern tip of Mumbai, SNARES can compute optimal strategy for the entire urban road network of Mumbai.
2013_7_teamcore_fp066-jain.pdf