2012
Rong Yang, Fei Fang, Albert Xin Jiang, Karthik Rajagopal, Milind Tambe, and Rajiv Maheswaran. 2012. “
Modeling Human Bounded Rationality to Improve Defender Strategies in Network Security Games .” In Workshop on Human-Agent Interaction Design and Models (HAIDM) at AAMAS .
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 perfectrationality 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_32_teamcore_paper_6_haidm.pdf Matthew Brown, Bo An, Christopher Kiekintveld, Fernando Ordonez, and Milind Tambe. 2012. “
Multi-Objective Optimization for Security Games .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractThe burgeoning area of security games has focused on real-world
domains where security agencies protect critical infrastructure from
a diverse set of adaptive adversaries. There are security domains
where the payoffs for preventing the different types of adversaries
may take different forms (seized money, reduced crime, saved lives,
etc) which are not readily comparable. Thus, it can be difficult to
know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multiobjective optimization. Instead of a single optimal solution, MOSGs
have a set of Pareto optimal (non-dominated) solutions referred
to as the Pareto frontier. The Pareto frontier can be generated
by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives.
Our contributions include: (i) an algorithm, Iterative -Constraints,
for generating the sequence of CSOPs; (ii) an exact approach for
solving an MILP formulation of a CSOP (which also applies to
multi-objective optimization in more general Stackelberg games);
(iii) heuristics that achieve speedup by exploiting the structure of
security games to further constrain a CSOP; (iv) an approximate
approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees.
Additional contributions of this paper include proofs on the level
of approximation and detailed experimental evaluation of the proposed approaches.
2012_19_teamcore_aamas2012multi_camerareadyfinal3.pdf Matthew Brown, Bo An, Christopher Kiekintveld, Fernando Ordonez, and Milind Tambe. 2012. “
Multi-Objective Optimization for Security Games .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractThe burgeoning area of security games has focused on real-world
domains where security agencies protect critical infrastructure from
a diverse set of adaptive adversaries. There are security domains
where the payoffs for preventing the different types of adversaries
may take different forms (seized money, reduced crime, saved lives,
etc) which are not readily comparable. Thus, it can be difficult to
know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multiobjective optimization. Instead of a single optimal solution, MOSGs
have a set of Pareto optimal (non-dominated) solutions referred
to as the Pareto frontier. The Pareto frontier can be generated
by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives.
Our contributions include: (i) an algorithm, Iterative -Constraints,
for generating the sequence of CSOPs; (ii) an exact approach for
solving an MILP formulation of a CSOP (which also applies to
multi-objective optimization in more general Stackelberg games);
(iii) heuristics that achieve speedup by exploiting the structure of
security games to further constrain a CSOP; (iv) an approximate
approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees.
Additional contributions of this paper include proofs on the level
of approximation and detailed experimental evaluation of the proposed approaches.
2012_17_teamcore_aamas2012multifinal2.pdf Matthew P. Johnson, Fei Fang, and Milind Tambe. 2012. “
Patrol Strategies to Maximize Pristine Forest Area .” In Conference on Artificial Intelligence (AAAI) .
AbstractIllegal extraction of forest resources is fought, in many developing countries, by patrols that try to make this activity
less profitable, using the threat of confiscation. With a limited
budget, officials will try to distribute the patrols throughout
the forest intelligently, in order to most effectively limit extraction. Prior work in forest economics has formalized this
as a Stackelberg game, one very different in character from
the discrete Stackelberg problem settings previously studied
in the multiagent literature. Specifically, the leader wishes to
minimize the distance by which a profit-maximizing extractor will trespass into the forest—or to maximize the radius
of the remaining “pristine” forest area. The follower’s costbenefit analysis of potential trespass distances is affected by
the likelihood of being caught and suffering confiscation.
In this paper, we give a near-optimal patrol allocation algorithm and a 1/2-approximation algorithm, the latter of which
is more efficient and yields simpler, more practical patrol allocations. Our simulations indicate that these algorithms substantially outperform existing heuristic allocations.
2012_29_teamcore_forestaaai.pdf Jason Tsai, Emma Bowring, Stacy Marsella, Wendy Wood, and Milind Tambe. 2012. “
Preliminary Exploration of Agent-Human Emotional Contagion via Static Expressions .” In Workshop on Emotional and Empathic Agents (EEA) at AAMAS .
AbstractIn social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [13]. 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.
In this paper, we perform a battery of experiments to explore the
existence of agent-human emotional contagion. The first study is a
between-subjects design, wherein subjects were shown an image of
a character’s face with either a neutral or happy expression. Findings indicate that even a still image induces a very strong increase
in self-reported happiness between Neutral and Happy conditions
with all characters tested and, to our knowledge, is the first ever
study explicitly showing emotional contagion from a virtual agent
to a human. We also examine the effects of participant gender, participant ethnicity, character attractiveness, and perceived character
happiness and find that only perceived character happiness has a
substantial impact on emotional contagion.
In a second study, we examine the effect of a virtual character’s
presence in a strategic situation by presenting subjects with a modernized Stag Hunt game. Our experiments show that the contagion
effect is substantially dampened and does not cause a consistent impact on behavior. A third study explores the impact of the strategic
decision within the Stag Hunt and conducts the same experiment
using a description of the same strategic situation with the decision
already made. We find that the emotional impact returns again,
particularly for women, implying that the contagion effect is substantially lessened in the presence of a strategic decision.
2012_31_teamcore_ec_-_cameraready.pdf Bo An, Eric Shieh, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, and Garrett Meyer. 2012. “
PROTECT - A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard .” AI Magazine 33 (4), Pp. 96-110.
AbstractWhile three deployed applications of game theory for security have recently been reported, we as a community of agents
and AI researchers remain in the early stages of these deployments; there is a continuing need to understand the core
principles for innovative security applications of game theory. Towards that end, this paper presents PROTECT, a gametheoretic system deployed by the United States Coast Guard
(USCG) in the Port of Boston for scheduling their patrols.
USCG has termed the deployment of PROTECT in Boston
a success; PROTECT is currently being tested in the Port of
New York, with the potential for nationwide deployment.
PROTECT is premised on an attacker-defender Stackelberg
game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary
rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary’s behavior — to the
best of our knowledge, this is the first real-world deployment
of the QR model. Second, to improve PROTECT’s efficiency, we generate a compact representation of the defender’s
strategy space, exploiting equivalence and dominance. Third,
we show how to practically model a real maritime patrolling
problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT’s QR model more robustly handles real-world uncertainties than a perfect rationality model.
Finally, in evaluating PROTECT, this paper for the first time
provides real-world data: (i) comparison of human-generated
vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.
2012_46_teamcore_protect_aim.pdf Eric Shieh, Bo An, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, and Garrett Meyer. 2012. “
PROTECT: A Deployed Game Theoretic System to Protect the Ports of the United States .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractWhile three deployed applications of game theory for security have
recently been reported at AAMAS [12], we as a community remain in the early stages of these deployments; there is a continuing
need to understand the core principles for innovative security applications of game theory. Towards that end, this paper presents
PROTECT, a game-theoretic system deployed by the United States
Coast Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed the deployment of PROTECT in Boston a
success, and efforts are underway to test it in the port of New York,
with the potential for nationwide deployment.
PROTECT is premised on an attacker-defender Stackelberg game
model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in
previous work, relying instead on a quantal response (QR) model
of the adversary’s behavior — to the best of our knowledge, this
is the first real-world deployment of the QR model. Second, to
improve PROTECT’s efficiency, we generate a compact representation of the defender’s strategy space, exploiting equivalence and
dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT’s QR model more robustly
handles real-world uncertainties than a perfect rationality model.
Finally, in evaluating PROTECT, this paper for the first time provides real-world data: (i) comparison of human-generated vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.
2012_8_teamcore_protect_aamas_2012_camera_ready_final2_20120109.pdf Eric Shieh, Bo An, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, and Garrett Meyer. 2012. “
PROTECT: An Application of Computational Game Theory for the Security of the Ports of the United States .” In Conference on Artificial Intelligence (AAAI) Spotlight Track .
AbstractBuilding upon previous security applications of computational game theory, this paper presents PROTECT, a gametheoretic system deployed by the United States Coast Guard
(USCG) in the port of Boston for scheduling their patrols.
USCG has termed the deployment of PROTECT in Boston a
success, and efforts are underway to test it in the port of New
York, with the potential for nationwide deployment.
PROTECT is premised on an attacker-defender Stackelberg
game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary
rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary’s behavior — to the
best of our knowledge, this is the first real-world deployment
of the QR model. Second, to improve PROTECT’s efficiency,
we generate a compact representation of the defender’s strategy space, exploiting equivalence and dominance. Third,
we show how to practically model a real maritime patrolling
problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT’s QR model more robustly handles real-world uncertainties than a perfect rationality model.
Finally, in evaluating PROTECT, this paper provides realworld data: (i) comparison of human-generated vs PROTECT
security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.
2012_26_teamcore_shieh_protect_aaai_talk_20120414.pdf Eric Shieh, Bo An, Rong Yang, Milind Tambe, Craig Baldwin, Joseph DiRenzo, Ben Maule, Garrett Meyer, and Kathryn Moretti. 2012. “
PROTECT in the Ports of Boston, New York and Beyond: Experiences in Deploying Stackelberg Security Games with Quantal Response.” In . Springer.
AbstractWhile three deployed applications of game theory for security have recently been reported at AAMAS [21], we as a community remain in the early stages
of these deployments; there is a continuing need to understand the core principles
for innovative security applications of game theory. Towards that end, this chapter
presents PROTECT, a game-theoretic system deployed by the United States Coast
Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed
the deployment of PROTECT in Boston a success, and efforts are underway to test
it in the port of New York, with the potential for nationwide deployment.
PROTECT is premised on an attacker-defender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of
perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary’s behavior. To the best of our knowledge, this is
the first real-world deployment of the QR model. Second, to improve PROTECT’s
efficiency, we generate a compact representation of the defender’s strategy space,
exploiting equivalence and dominance. Third, we show how to practically model a
real maritime patrolling problem as a Stackelberg game. Fourth, our experimental
results illustrate that PROTECT’s QR model more robustly handles real-world uncertainties than a perfect rationality model does. Finally, in evaluating PROTECT,
this chapter provides real-world data: (i) comparison of human-generated vs. PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s
(human mock attackers) analysis.
2012_38_teamcore_protectchapter_20120601.pdf James Pita, Richard John, Rajiv Maheswaran, Milind Tambe, and Sarit Kraus. 2012. “
A Robust Approach to Addressing Human Adversaries in Security Games .” In European Conference on Artificial Intelligence (ECAI).
AbstractGame-theoretic approaches have been proposed for addressing the complex problem of assigning limited security resources
to protect a critical set of targets. However, many of the standard
assumptions fail to address human adversaries who security forces
will likely face. To address this challenge, previous research has
attempted to integrate models of human decision-making into the
game-theoretic algorithms for security settings. The current leading
approach, based on experimental evaluation, is derived from a wellfounded solution concept known as quantal response and is known
as BRQR. One critical difficulty with opponent modeling in general
is that, in security domains, information about potential adversaries
is often sparse or noisy and furthermore, the games themselves are
highly complex and large in scale. Thus, we chose to examine a completely new approach to addressing human adversaries that avoids
the complex task of modeling human decision-making. We leverage
and modify robust optimization techniques to create a new type of
optimization where the defender’s loss for a potential deviation by
the attacker is bounded by the distance of that deviation from the
expected-value-maximizing strategy. To demonstrate the advantages
of our approach, we introduce a systematic way to generate meaningful reward structures and compare our approach with BRQR in the
most comprehensive investigation to date involving 104 security settings where previous work has tested only up to 10 security settings.
Our experimental analysis reveals our approach performing as well
as or outperforming BRQR in over 90% of the security settings tested
and we demonstrate significant runtime benefits. These results are in
favor of utilizing an approach based on robust optimization in these
complex domains to avoid the difficulties of opponent modeling.
2012_37_teamcore_match_ecai_final2.pdf James Pita, Richard John, Rajiv Maheswaran, Milind Tambe, Rong Yang, and Sarit Kraus. 2012. “
A Robust Approach to Addressing Human Adversaries in Security Games: Extended Abstract .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Short paper).
AbstractWhile game-theoretic approaches have been proposed for addressing complex security resource allocation problems, many of the
standard game-theoretic assumptions fail to address human adversaries who security forces will likely face. To that end, approaches
have been proposed that attempt to incorporate better models of human decision-making in these security settings. We take a new approach where instead of trying to create a model of human decisionmaking, we leverage ideas from robust optimization techniques. In
addition, we extend our approach and the previous best performing
approach to also address human anchoring biases under limited observation conditions. To evaluate our approach, we perform a comprehensive examination comparing the performance of our new approach against the current leading approaches to addressing human
adversaries. Finally, in our experiments we take the first ever analysis of some demographic information and personality measures
that may influence decision making in security games.
2012_13_teamcore_matchextendedabstract.pdf Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe, Farrokh Jazizadeh, Geoffrey Kavulya, Laura Klein, Burcin Becerik-Gerber, Timothy Hayes, and Wendy Wood. 2012. “
SAVES: A Sustainable Multiagent Application to Conserve Building Energy Considering Occupants .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) .
AbstractThis paper describes an innovative multiagent system called
SAVES with the goal of conserving energy in commercial buildings. We specifically focus on an application to be deployed in
an existing university building that provides several key novelties:
(i) jointly performed with the university facility management team,
SAVES is based on actual occupant preferences and schedules, actual energy consumption and loss data, real sensors and hand-held
devices, etc.; (ii) it addresses novel scenarios that require negotiations with groups of building occupants to conserve energy; (iii) it
focuses on a non-residential building, where human occupants do
not have a direct financial incentive in saving energy and thus requires a different mechanism to effectively motivate occupants; and
(iv) SAVES uses a novel algorithm for generating optimal MDP
policies that explicitly consider multiple criteria optimization (energy and personal comfort) as well as uncertainty over occupant
preferences when negotiating energy reduction – this combination
of challenges has not been considered in previous MDP algorithms.
In a validated simulation testbed, we show that SAVES substantially reduces the overall energy consumption compared to the existing control method while achieving comparable average satisfaction levels for occupants. As a real-world test, we provide results of
a trial study where SAVES is shown to lead occupants to conserve
energy in real buildings.
2012_9_teamcore_aamas12-saves-camera-ready-final.pdf Manish Jain, Bo An, and Milind Tambe. 2012. “
Security Games Applied to Real-World: Research Contributions and Challenges.” In In S. Jajodia, A.K. Ghosh, V.S. Subramanian, V. Swarup, C. Wang, and X. S. Wang editors Moving Target Defense II: Application of Game Theory and Adversarial Modeling, Springer .
AbstractThe goal of this chapter is to introduce a challenging real-world 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 problem arena.
Furthermore, we present key ideas and algorithms for solving and understanding
the characteristics large-scale real-world security games, and then present some key
open research challenges in this area.
2012_35_teamcore_manish.pdf Thanh H. Nguyen, Jason Tsai, Albert Jiang, Emma Bowring, Rajiv Maheswaran, and Milind Tambe. 2012. “
Security Games on Social Networks .” In AAAI Fall Symposium, 2012 .
AbstractMany real-world problems exhibit competitive situations in
which a defender (a defending agent, agency, or organization) has to address misinformation spread by its adversary,
e.g., health organizations cope with vaccination-related misinformation provided by anti-vaccination groups. The rise of
social networks has allowed misinformation to be easily and
quickly diffused to a large community. Taking into account
knowledge of its adversary’s actions, the defender has to seek
efficient strategies to limit the influence of the spread of misinformation by the opponent.
In this paper, we address this problem as a blocking influence
maximization problem using a game-theoretic approach. Two
players strategically select a number of seed nodes in the social network that could initiate their own influence propagation. While the adversary attempts to maximize its negative
influence, the defender tries to minimize this influence. We
represent the problem as a zero-sum game and apply the Double Oracle algorithm to solve the game in combination with
various heuristics for oracle phases. Our experimental results
reveal that by using the game theoretic approach, we are able
to significantly reduce the negative influence in comparison
to when the defender does not do anything. In addition, we
propose using an approximation of the payoff matrix, making
the algorithms scalable to large real-world networks.
2012_41_teamcore_security_games_on_social_networks.pdf Bo An, David Kempe, Christopher Kiekintveld, Eric Shieh, Satinder Singh, Milind Tambe, and Yevgeniy Vorobeychik. 2012. “
Security Games with Limited Surveillance .” In Conference on Artificial Intelligence (AAAI) .
AbstractRandomized first-mover strategies of Stackelberg games
are used in several deployed applications to allocate
limited resources for the protection of critical infrastructure.
Stackelberg games model the fact that a strategic attacker can
surveil and exploit the defender’s strategy, and randomization
guards against the worst effects by making the defender less
predictable. In accordance with the standard game-theoretic
model of Stackelberg games, past work has typically assumed
that the attacker has perfect knowledge of the defender’s
randomized strategy and will react correspondingly. In light
of the fact that surveillance is costly, risky, and delays an
attack, this assumption is clearly simplistic: attackers will
usually act on partial knowledge of the defender’s strategies.
The attacker’s imperfect estimate could present opportunities
and possibly also threats to a strategic defender.
In this paper, we therefore begin a systematic study of
security games with limited surveillance. We propose
a natural model wherein an attacker forms or updates a
belief based on observed actions, and chooses an optimal
response. We investigate the model both theoretically and
experimentally. In particular, we give mathematical programs
to compute optimal attacker and defender strategies for a
fixed observation duration, and show how to use them to
estimate the attacker’s observation durations. Our experimental results show that the defender can achieve significant
improvement in expected utility by taking the attacker’s
limited surveillance into account, validating the motivation
of our work.
2012_25_teamcore_boan_aaai12.pdf Bo An, David Kempe, Christopher Kiekintveld, Eric Shieh, Satinder Singh, Milind Tambe, and Yevgeniy Vorobeychik. 2012. “
Security Games with Limited Surveillance: An Initial Report.” In AAAI Spring Symposium on Game Theory for Security, Sustainability and Health.
AbstractStackelberg games have been used in several deployed applications of game theory to make recommendations for allocating limited resources for protecting critical infrastructure. The resource allocation strategies are randomized to
prevent a strategic attacker from using surveillance to learn
and exploit patterns in the allocation. An important limitation of previous work on security games is that it typically
assumes that attackers have perfect surveillance capabilities,
and can learn the exact strategy of the defender. We introduce
a new model that explicitly models the process of an attacker observing a sequence of resource allocation decisions and
updating his beliefs about the defender’s strategy. For this
model we present computational techniques for updating the
attacker’s beliefs and computing optimal strategies for both
the attacker and defender, given a specific number of observations. We provide multiple formulations for computing the
defender’s optimal strategy, including non-convex programming and a convex approximation. We also present an approximate method for computing the optimal length of time
for the attacker to observe the defender’s strategy before attacking. Finally, we present experimental results comparing
the efficiency and runtime of our methods.
2012_7_teamcore_aaaiss_final.pdf J. Tsai, E. Bowring, S. Marsella, W. Wood, and M. Tambe. 2012. “
A Study of Emotional Contagion with Virtual Characters .” In International Conference on Intelligent Virtual Agents (IVA) (short paper) .
AbstractIn social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s
emotions [10]. In this paper, we perform a battery of experiments to explore
the existence of agent-human emotional contagion. The first study is a betweensubjects design, wherein subjects were shown an image of a character’s face with
either a neutral or happy expression. Findings indicate that even a still image
induces a very strong increase in self-reported happiness between Neutral and
Happy conditions with all characters tested.
In a second study, we examine the effect of a virtual character’s presence in a
strategic situation by presenting subjects with a modernized Stag Hunt game.
Our experiments show that the contagion effect is substantially dampened and
does not cause a consistent impact on behavior. A third study explores the impact
of the strategic decision within the Stag Hunt and conducts the same experiment
using a description of the same strategic situation with the decision already made.
We find that the emotional impact returns, implying that the contagion effect is
substantially lessened in the presence of a strategic decision.
2012_39_teamcore_20120625_iva_camera_ready_v2.pdf Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe, Farrokh Jazizadeh, Geoffrey Kavulya, Laura Klein, Burcin Becerik-Gerber, Timothy Hayes, and Wendy Wood. 2012. “
Sustainable Multiagent Application to Conserve Energy .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Demonstration Track .
AbstractLimited availability of energy resources has motivated the need
for developing efficient measures of conserving energy. Conserving energy in commercial buildings is an important goal since these
buildings consume significant amount of energy, e.g., 46.2% of
all building energy and 18.4% of total energy consumption in the
US [1]. This demonstration focuses on a novel application to be
deployed at Ralph & Goldy Lewis Hall (RGL) at the University
of Southern California as a practical research testbed to optimize
multiple competing objectives: i) energy use in the building; ii)
occupants’ comfort level; and iii) practical usage considerations.
This demonstration complements our paper in the AAMAS innovative applications track [4], presenting a novel multiagent building application for sustainability called SAVES (Sustainable multiAgent systems for optimizing Variable objectives including Energy
and Satisfaction). This writeup will provide a high-level overview
of SAVES and focus more on the proposed demonstration, but readers are referred to [4] for a more technical description. SAVES provides three key contributions: (i) jointly performed with the university facility management team, our research is based on actual
building and occupant data as well as real sensors and devices, etc.;
(ii) it focuses on non-residential buildings, where human occupants
do not have a direct financial incentive in saving energy; and (iii)
SAVES uses a novel algorithm for generating optimal BM-MDP
(Bounded parameter Multi-objective MDP) policies.
We demonstrate SAVES to show how to achieve significant energy savings and comparable average satisfaction level of occupants while emphasizing the interactive aspects of our application.
2012_21_teamcore_aamas12-energy-demo-camera-ready.pdf Albert Xin Jiang, Zhengyu Yin, Matthew P. Johnson, Christopher Kiekintveld, Kevin Leyton-Brown, Tuomas Sandholm, and Milind Tambe. 2012. “
Towards Optimal Patrol Strategies for Fare Inspection in Transit Systems .” In AAAI Spring Symposium on Game Theory for Security, Sustainability and Health .
AbstractIn some urban transit systems, passengers are legally required
to purchase tickets before entering but are not physically
forced to do so. Instead, patrol units move about through
the transit system, inspecting tickets of passengers, who face
fines for fare evasion. This setting yields the problem of computing optimal patrol strategies satisfying certain temporal
and spacial constraints, to deter fare evasion and hence maximize revenue. In this paper we propose an initial model of
this problem as a leader-follower Stackelberg game. We then
formulate an LP relaxation of this problem and present initial
experimental results using real-world ridership data from the
Los Angeles Metro Rail system.
2012_10_teamcore_trustsrail.pdf Jun-young Kwak, Pradeep Varakantham, Rajiv Maheswaran, Milind Tambe, Timothy Hayes, Wendy Wood, and Burcin Becerik-Gerber. 2012. “
Towards Robust Multi-objective Optimization Under Model Uncertainty for Energy Conservation .” In Workshop on Agent Technologies for Energy Systems (ATES) at AAMAS .
Abstractto the significant growth in energy usage. Building multiagent systems for real-world energy applications raises several research challenges regarding scalability, optimizing multiple competing objectives, model uncertainty, and complexity in deploying the system.
Motivated by these challenges, this paper proposes a new approach
to effectively conserve building energy. This work contributes to a
very new area that requires considering large-scale multi-objective
optimization as well as uncertainty over occupant preferences when
negotiating energy reduction. There are three major contributions.
We (i) develop a new method called HRMM to compute robust
solutions in practical situations; (ii) experimentally show that obtained strategies from HRMM converge to near-optimal solutions;
and (iii) provide a systematic way to tightly incorporate the insights
from human subject studies into our computational model and algorithms. The HRMM method is verified in a validated simulation
testbed in terms of energy savings and comfort levels of occupants
2012_30_teamcore_ates12-workshop-camera-ready-final.pdf