2015
D. J. Gerber, E. Pantazis, L. S. Marcolino, and A. Heydarian. 2015. “
A Multi-Agent Systems for Design Simulation Framework: Experiments with Virtual-Physical-Social Feedback for Architecture .” In Symposium on Simulation for Architecture and Urban Design (SimAUD 2015).
AbstractThis paper presents research on the development of multiagent systems (MAS) for integrated and performance driven
architectural design. It presents the development of a
simulation framework that bridges architecture and
engineering, through a series of multi-agent based
experiments. The research is motivated to combine multiple
design agencies into a system for managing and optimizing
architectural form, across multiple objectives and contexts.
The research anticipates the incorporation of feedback from
real world human behavior and user preferences with physics
based structural form finding and environmental analysis
data. The framework is a multi-agent system that provides
design teams with informed design solutions, which
simultaneously optimize and satisfy competing design
objectives. The initial results for building structures are
measured in terms of the level of lighting improvements and
qualitatively in geometric terms. Critical to the research is
the elaboration of the system and the feedback loops that are
possible when using the multi-agent systems approach.
2015_18_teamcore_simaud2015.pdf Eric Shieh. 2015. “
Not a Lone Ranger: Unleashing Defender Teamwork in Security Games ”.
AbstractGame theory has become an important research area in handling complex security resource allocation and patrolling problems. Stackelberg Security Games (SSGs) have been used in modeling these types of problems via a defender and an attacker(s). Despite recent successful real-world deployments of SSGs, scale-up to handle defender teamwork remains a fundamental challenge in this field. The latest techniques do not scale-up to domains where multiple defenders must coordinate time-dependent joint activities. To address this challenge, my thesis presents algorithms for solving defender teamwork in SSGs in two phases. As a first step, I focus on domains without execution uncertainty, in modeling and solving SSGs that incorporate teamwork among defender resources via three novel features: (i) a column-generation approach that uses an ordered network of nodes (determined by solving the traveling salesman problem) to generate individual defender strategies; (ii) exploitation of iterative reward shaping of multiple coordinating defender units to generate coordinated strategies; (iii) generation of tighter upper-bounds for pruning by solving security games that only abide by key scheduling constraints. In the second stage of my thesis, I address execution uncertainty among defender resources that arises from the real world by integrating the powerful teamwork mechanisms offered by decentralized Markov Decision Problems (Dec-MDPs) into security games. My thesis offers the following novel contributions: (i) New model of security games with defender teams that coordinate under uncertainty; (ii) New algorithm based on column generation that utilizes Decentralized Markov Decision Processes (Dec-MDPs) to generate defender strategies that incorporate uncertainty; (iii) New techniques to handle global events (when one or more agents may leave the system) during defender execution; (iv) Heuristics that help scale up in the number of targets and resources to handle real-world scenarios; (v) Exploration of the robustness of randomized pure strategies. Different mechanisms, from both solving situations with and without execution uncertainty, may be used depending on the features of the domain. This thesis opens the door to a powerful combination of previous work in multiagent systems on teamwork and security games.
2015_19_teamcore_shieh_thesis_20150324.pdf L. S. Marcolino, H. Xu, A.X. Jiang, M. Tambe, and E. Bowring. 2015. “
The Power of Teams that Disagree: Team Formation in Large Action Spaces .” In Coordination, Organizations, Institutions and Norms in Agent Systems X. Springer-Verlag Lecture Notes in AI, 2015.
AbstractRecent work has shown that diverse teams can outperform a uniform
team made of copies of the best agent. However, there are fundamental questions
that were never asked before. When should we use diverse or uniform teams?
How does the performance change as the action space or the teams get larger?
Hence, we present a new model of diversity, where we prove that the performance
of a diverse team improves as the size of the action space increases. Moreover, we
show that the performance converges exponentially fast to the optimal one as we
increase the number of agents. We present synthetic experiments that give further
insights: even though a diverse team outperforms a uniform team when the size
of the action space increases, the uniform team will eventually again play better
than the diverse team for a large enough action space. We verify our predictions
in a system of Go playing agents, where a diverse team improves in performance
as the board size increases, and eventually overcomes a uniform team.1
2015_32_teamcore_coin2014book.pdf Amulya Yadav, Leandro Soriano Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “
PSINET: Aiding HIV Prevention Amongst Homeless Youth by Planning Ahead .” AI Magazine.
AbstractHomeless youth are prone to Human Immunodeficiency
Virus (HIV) due to their engagement in high risk behavior such as unprotected sex, sex under influence of
drugs, etc. Many non-profit agencies conduct interventions to educate and train a select group of homeless
youth about HIV prevention and treatment practices and
rely on word-of-mouth spread of information through
their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed
PSINET, a decision support system to aid the agencies
in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure
and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization;
and (iii) it provides algorithmic advances to allow high
quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ∼60% more information spread over the current state-of-the-art. PSINET
was developed in collaboration with My Friend’s Place
(a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.
2015_25_teamcore_aimag_yadav.pdf Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “
PSINET - An Online POMDP Solver for HIV Prevention in Homeless Populations .” In In AAAI-15 Workshop on Planning, Search, and Optimization (PlanSOpt-15).
AbstractHomeless youth are prone to Human Immunodeficiency
Virus (HIV) due to their engagement in high risk behavior such as unprotected sex, sex under influence of
drugs, etc. Many non-profit agencies conduct interventions to educate and train a select group of homeless
youth about HIV prevention and treatment practices and
rely on word-of-mouth spread of information through
their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed
PSINET, a decision support system to aid the agencies
in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure
and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization;
and (iii) it provides algorithmic advances to allow high
quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ∼60% more information spread over the current state-of-the-art. PSINET
was developed in collaboration with My Friend’s Place
(a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.
2015_4_teamcore_yadav.pdf Arjun Tambe and Thanh Nguyen. 2015. “
Robust Resource Allocation in Security Games and Ensemble Modeling of Adversary Behavior .” In ACM Symposium on Applied Computing (ACM SAC 2015) Track on Intelligent Robotics and Multi-Agent Systems (IRMAS).
AbstractGame theoretic algorithms have been used to optimize the
allocation of security resources to improve the protection of critical
infrastructure against threats when limits on security resources
prevent full protection of all targets. Past approaches have assumed
adversaries will always behave to maximize their expected utility,
failing to address real-world adversaries who are not perfectly
rational. Instead, adversaries may be boundedly rational, i.e., they
generally act to increase their expected value but do not
consistently maximize it. A successful approach to addressing
bounded adversary rationality has been a robust approach that does
not explicitly model adversary behavior. However, these robust
algorithms implicitly rely on an efficiently computable weak model
of adversary behavior, which does not necessarily match adversary
behavior trends. We therefore propose a new robust algorithm that
provides a more refined model of adversary behavior that retains
the advantage of efficient computation. We also develop an
ensemble method used to tune the algorithm’s parameters, and
compare this method’s accuracy in predicting adversary behavior
to previous work. We test these contributions in security games
against human subjects to show the advantages of our approach.
2015_5_teamcore_arjun_paper.pdf Yundi Qian, William B. Haskell, and Milind Tambe. 2015. “
Robust Strategy against Unknown Risk-averse Attackers in Security Games .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).
AbstractStackelberg security games (SSGs) are now established as a powerful tool in security domains. In this paper, we consider a new
dimension of security games: the risk preferences of the attacker.
Previous work assumes a risk-neutral attacker that maximizes his
expected reward. However, extensive studies show that the attackers in some domains are in fact risk-averse, e.g., terrorist groups
in counter-terrorism domains. The failure to incorporate the risk
aversion in SSG models may lead the defender to suffer significant
losses. Additionally, defenders are uncertain about the degree of
attacker’s risk aversion. Motivated by this challenge this paper provides the following five contributions: (i) we propose a novel model
for security games against risk-averse attackers with uncertainty in
the degree of their risk aversion; (ii) we develop an intuitive MIBLP formulation based on previous security games research, but
find that it finds locally optimal solutions and is unable to scale up;
(iii) based on insights from our MIBLP formulation, we develop
our scalable BeRRA algorithm that finds globally ǫ-optimal solutions; (iv) our BeRRA algorithm can also be extended to handle
other risk-aware attackers, e.g., risk-seeking attackers; (v) we show
that we do not need to consider attacker’s risk attitude in zero-sum
games.
2015_12_teamcore_yundi_aamas2015.pdf Haifeng Xu, Albert X. Jiang, Arunesh Sinha, Zinovi Rabinovich, Shaddin Dughmi, and Milind Tambe. 2015. “
Security Games with Information Leakage: Modeling and Computation .” In International Joint Conference on Artificial Intelligence (IJCAI 2015).
AbstractMost models of Stackelberg security games assume that the attacker only knows the defender’s mixed strategy, but is not able to observe (even partially) the instantiated pure strategy. Such partial observation of the deployed pure strategy – an issue we refer to as information
leakage – is a significant concern in practical applications. While previous research on patrolling
games has considered the attacker’s real-time surveillance, our settings, therefore models and
techniques, are fundamentally different. More specifically, after describing the information leakage model, we start with an LP formulation to compute the defender’s optimal strategy in the
presence of leakage. Perhaps surprisingly, we show that a key subproblem to solve this LP
(more precisely, the defender oracle) is NP-hard even for the simplest of security game models.
We then approach the problem from three possible directions: efficient algorithms for restricted
cases, approximation algorithms, and heuristic algorithms for sampling that improves upon the
status quo. Our experiments confirm the necessity of handling information leakage and the
advantage of our algorithms.
2015_22_teamcore_infor_leakage.pdf L. S. Marcolino and M. Tambe. 2015. “
Three Fundamental Pillars of Multi-agent Team Formation (Doctoral Consortium) .” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).
AbstractTeams of voting agents are a powerful tool for solving complex problems. When forming such teams, there are three
fundamental issues that must be addressed: (i) Selecting
which agents should form a team; (ii) Aggregating the opinions of the agents; (iii) Assessing the performance of a team.
In this thesis we address all these points.
2015_17_teamcore_aamas15dc.pdf L. S. Marcolino and M. Tambe. 2015. “
Unleashing the Power of Multi-agent Voting Teams (Doctoral Consortium) .” In International Joint Conference on Artificial Intelligence (IJCAI 2015).
AbstractTeams of voting agents have great potential in finding optimal solutions. However, there are fundamental challenges to effectively use such teams: (i)
selecting agents; (ii) aggregating opinions; (iii) assessing performance. I address all these challenges,
with theoretical and experimental contributions.
2015_26_teamcore_ijcai15dc.pdf D. Kar, F. Fang, F. Delle Fave, N. Sintov, M. Tambe, and A. Van Wissen. 2015. “
Effectiveness of Probability Perception Modeling and Defender Strategy Generation Algorithms in Repeated Stackelberg Games: An Initial Report.” In Computational Sustainability Workshop at AAAI15, Texas, Austin.
AbstractWhile human behavior models based on repeated Stackelberg games have been proposed for domains such as “wildlife crime” where there is repeated interaction between the defender and the adversary, there has been no empirical study with human subjects to show the effectiveness of such models. This paper presents an initial study based on extensive human subject experiments with participants on Amazon Mechanical Turk (AMT). Our findings include: (i) attackers may view the defender’s coverage probability in a non-linear fashion; specifically it follows an S-shaped curve, and (ii) there are significant losses in defender utility when strategies generated by existing models are deployed in repeated Stackelberg game settings against human subjects.
2015_7_teamcore_debarun_kar_aaai15_cs_workshop.pdf Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov, and Milind Tambe. 2015. “
A Game of Thrones: When Human Behavior Models Compete in Repeated Stackelberg Security Games.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).
AbstractSeveral competing human behavior models have been proposed to model and protect against boundedly rational adversaries in repeated Stackelberg security games (SSGs). However, these existing models fail to address three main issues which are extremely detrimental to defender performance. First, while they attempt to learn adversary behavior models from adversaries’ past actions (“attacks on targets”), they fail to take into account adversaries’ future adaptation based on successes or failures of these past actions. Second, they assume that sufficient data in the initial rounds will lead to a reliable model of the adversary. However, our analysis reveals that the issue is not the amount of data, but that there just is not enough of the attack surface exposed to the adversary to learn a reliable model. Third, current leading approaches have failed to include probability weighting functions, even though it is well known that human beings’ weighting of probability is typically nonlinear. The first contribution of this paper is a new human behavior model, SHARP, which mitigates these three limitations as follows: (i) SHARP reasons based on success or failure of the adversary’s past actions on exposed portions of the attack surface to model adversary adaptiveness; (ii) SHARP reasons about similarity between exposed and unexposed areas of the attack surface, and also incorporates a discounting parameter to mitigate adversary’s lack of exposure to enough of the attack surface; and (iii) SHARP integrates a non-linear probability weighting function to capture the adversary’s true weighting of probability. Our second contribution is a first “longitudinal study” – at least in the context of SSGs – of competing models in settings involving repeated interaction between the attacker and the defender. This study, where each experiment lasted a period of multiple weeks with individual sets of human subjects, illustrates the strengths and weaknesses of different models and shows the advantages of SHARP.
2015_11_teamcore_aamas15_fp85_crc.pdf Thanh H. Nguyen, Francesco M. Delle Fave, Debarun Kar, Aravind S. Lakshminarayanan, Amulya Yadav, Milind Tambe, Noa Agmon, Andrew J. Plumptre, Margaret Driciru, Fred Wanyama, and Aggrey Rwetsiba. 2015. “
Making the most of Our Regrets: Regret-based Solutions to Handle Payoff Uncertainty and Elicitation in Green Security Games.” In Conference on Decision and Game Theory for Security.
AbstractRecent research on Green Security Games (GSG), i.e., security games for the protection of wildlife, forest and fisheries, relies on the promise of an abundance of available data in these domains to learn adversary behavioral models and determine game payoffs. This research suggests that adversary behavior models (capturing bounded rationality) can be learned from real-world data on where adversaries have attacked, and that game payoffs can be determined precisely from data on animal densities. However, previous work has, as yet, failed to demonstrate the usefulness of these behavioral models in capturing adversary behaviors based on real-world data in GSGs. Previous work has also been unable to address situations where available data is insufficient to accurately estimate behavioral models or to obtain the required precision in the payoff values. In addressing these limitations, as our first contribution, this paper, for the first time, provides validation of the aforementioned adversary behavioral models based on real-world data from a wildlife park in Uganda. Our second contribution addresses situations where real-world data is not precise enough to determine exact payoffs in GSG, by providing the first algorithm to handle payoff uncertainty in the presence of adversary behavioral models. This algorithm is based on the notion of minimax regret. Furthermore, in scenarios where the data is not even sufficient to learn adversary behaviors, our third contribution is to provide a novel algorithm to address payoff uncertainty assuming a perfectly rational attacker (instead of relying on a behavioral model); this algorithm allows for a significant scaleup for large security games. Finally, to reduce the problems due to paucity of data, given mobile sensors such as Unmanned Aerial Vehicles (UAV), we introduce new payoff elicitation strategies to strategically reduce uncertainty.
2015_35_teamcore_gamesec2015_arrow.pdf Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “
Preventing HIV Spread in Homeless Populations Using PSINET.” In Conference on Innovative Applications of Artificial Intelligence (IAAI-15).
AbstractHomeless youth are prone to HIV due to their engagement in high risk behavior. Many agencies conduct interventions to educate/train a select group of homeless youth about HIV prevention practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s structure and in the evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ∼60% more information spread over the current state-of-the-art. PSINET was developed in collaboration with My Friend’s Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.
2015_6_teamcore_iaai15.pdf Fei Fang, Peter Stone, and Milind Tambe. 2015. “
When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing.” In International Joint Conference on Artificial Intelligence (IJCAI).
AbstractBuilding on the successful applications of Stackelberg Security Games (SSGs) to protect infrastructure, researchers have begun focusing on applying game theory to green security domains such as protection of endangered animals and fish stocks. Previous efforts in these domains optimize defender strategies based on the standard Stackelberg assumption that the adversaries become fully aware of the defender’s strategy before taking action. Unfortunately, this assumption is inappropriate since adversaries in green security domains often lack the resources to fully track the defender strategy. This paper (i) introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; (ii) provides algorithms to plan effective sequential defender strategies — such planning was absent in previous work; (iii) proposes a novel approach to learn adversary models that further improves defender performance; and (iv) provides detailed experimental analysis of proposed approaches.
2015_21_teamcore_ijcai2015_gsg_cameraready_withappendix.pdf 2014
Y. Vorobeychik, B. An, M. Tambe, and S. Singh. 6/2014. “
Computing Solutions in Infinite-Horizon Discounted Adversarial Patrolling Game .” In International Conference on Automated Planning and Scheduling (ICAPS).
AbstractStackelberg games form the core of a number of tools deployed for computing optimal patrolling strategies in adversarial domains, such as the US Federal Air Marshall Service
and the US Coast Guard. In traditional Stackelberg security
game models 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 show that this general modeling framework can be captured using adversarial
patrolling games (APGs) in which the defender sequentially
moves between targets, with moves constrained by a graph,
while the attacker can observe the defender’s current location
and his (stochastic) policy concerning future moves. We offer a very general model of infinite-horizon discounted adversarial patrolling games. Our first contribution is to show that
defender policies that condition only on the previous defense
move (i.e., Markov stationary policies) can be arbitrarily suboptimal for general APGs. We then offer a mixed-integer nonlinear programming (MINLP) formulation for computing optimal randomized policies for the defender that can condition on history of bounded, but arbitrary, length, as well as
a mixed-integer linear programming (MILP) formulation to
approximate these, with provable quality guarantees. Additionally, we present a non-linear programming (NLP) formulation for solving zero-sum APGs. We show experimentally
that MILP significantly outperforms the MINLP formulation,
and is, in turn, significantly outperformed by the NLP specialized to zero-sum games.
2014_12_teamcore_apg_icaps_1.pdf Benjamin Ford, Debarun Kar, Francesco M. Delle Fave, Rong Yang, and Milind Tambe. 5/2014. “
PAWS: Adaptive Game-theoretic Patrolling for Wildlife Protection (Demonstration).” In Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractEndangered species around the world are in danger of extinction from poaching. From the start of the 20th century, the African rhino population has dropped over 98% [4] and the global tiger population has dropped over 95% [5], resulting in multiple species extinctions in both groups. Species extinctions have negative consequences on local ecosystems, economies, and communities. To protect these species, countries have set up conservation agencies and national parks, such as Uganda’s Queen Elizabeth National Park (QENP). However, a common lack of funding for these agencies results in a lack of law enforcement resources to protect these large, rural areas. As an example of the scale of disparity, one wildlife crime study in 2007 reported an actual coverage density of one ranger per 167 square kilometers [2]. Because of the hazards involved (e.g., armed poachers, wild animals), rangers patrol in groups, further increasing the amount of area they are responsible for patrolling. Security game research has typically been concerned with combating terrorism, and this field has indeed benefited from a range of successfully deployed applications [1, 6]. These applications have enabled security agencies to make more efficient use of their limited resources. In this previous research, adversary data has been absent during the development of these solutions, and thus, it has been difficult to make accurate adversary behavior models during algorithm development. In a domain such as wildlife crime, interactions with the adversary are frequent and repeated, thus enabling conservation agencies to collect data. This presence of data enables security game researchers to begin developing algorithms that incorporate this data into, potentially, more accurate behavior models and consequently better security solutions. Developed in conjunction with staff at QENP, the Protection Assistant for Wildlife Security (PAWS) generates optimized defender strategies for use by park rangers [7]. Due to the repeated nature of wildlife crime, PAWS is able to leverage crime event data - a previously unrealized capability in security games research. Thus, PAWS implements a novel adaptive algorithm that processes crime event data, builds multiple human behavior models, and, based on those models, predicts where adversaries will attack next. These predictions are then used to generate a patrol strategy for the rangers (i.e., a set of patrol waypoints) that can be viewed on a GPS unit. Against this background, the demonstration presented in this paper introduces two contributions. First, we present the PAWS system which incorporates the algorithm in [7] into a scheduling system and a GPS visualizer. Second, we present a software interface to run a number of human subject experiments (HSE) to evaluate and improve the efficacy of PAWS before its deployment in QENP. By conducting these HSEs, we can (i) test the PAWS algorithms with repeated interactions with humans, thus providing a more realistic testing environment than in its previous simulations; (ii) generate data that can be used to initialize PAWS’s human behavior models for deployment, and (iii) compare the current PAWS algorithms’ performance to alternatives and determine if additional improvements are needed prior to deployment. To provide proper context for the presentation, this paper also presents a brief overview of the PAWS system data flow and its adaptive algorithms. The demonstration will engage audience members by having them participate in the HSEs and using the GPS unit to visualize a patrol schedule in QENP.
2014_13_teamcore_de12_ford.pdf Matthew Brown, William B. Haskell, and Milind Tambe. 2014. “
Addressing Scalability and Robustness in Security Games with Multiple Boundedly Rational Adversaries .” In Conference on Decision and Game Theory for Security (GameSec).
AbstractBoundedly rational human adversaries pose a serious challenge to security because they deviate from the classical assumption of
perfect rationality. An emerging trend in security game research addresses this challenge by using behavioral models such as quantal response (QR) and subjective utility quantal response (SUQR). These
models improve the quality of the defender’s strategy by more accurately
modeling the decisions made by real human adversaries. Work on incorporating human behavioral models into security games has typically followed two threads. The first thread, scalability, seeks to develop efficient
algorithms to design patrols for large-scale domains that protect against
a single adversary. However, this thread cannot handle the common situation of multiple adversary types with heterogeneous behavioral models.
Having multiple adversary types introduces considerable uncertainty into
the defender’s planning problem. The second thread, robustness, uses either Bayesian or maximin approaches to handle this uncertainty caused
by multiple adversary types. However, the robust approach has so far
not been able to scale up to complex, large-scale security games. Thus,
each of these two threads alone fails to work in key real world security
games. Our present work addresses this shortcoming and merges these
two research threads to yield a scalable and robust algorithm, MIDAS
(MaxImin Defense Against SUQR), for generating game-theoretic patrols
to defend against multiple boundedly rational human adversaries. Given
the size of the defender’s optimization problem, the key component of
MIDAS is incremental cut and strategy generation using a master/slave
optimization approach. Innovations in MIDAS include (i) a maximin
mixed-integer linear programming formulation in the master and (ii) a
compact transition graph formulation in the slave. Additionally, we provide a theoretical analysis of our new model and report its performance
in simulations. In collaboration with the United States Coast Guard
(USCG), we consider the problem of defending fishery stocks from illegal fishing in the Gulf of Mexico and use MIDAS to handle heterogeneity
in adversary types (i.e., illegal fishermen) in order to construct robust
patrol strategies for USCG assets.
2014_31_teamcore_brown_game_sec2014.pdf L. S. Marcolino, B. Kolev, S. Price, S. P. Veetil, D. Gerber, J. Musil, and M. Tambe. 2014. “
Aggregating Opinions to Design Energy-Efficient Buildings .” In 8th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2014).
AbstractIn this research-in-progress paper we present a new real
world domain for studying the aggregation of different
opinions: early stage architectural design of buildings.
This is an important real world application, not only
because building design and construction is one of the
world’s largest industries measured by global expenditures, but also because the early stage design decision
making has a significant impact on the energy consumption of buildings. We present a mapping between the domain of architecture and engineering research and that
of the agent models present in the literature. We study
the importance of forming diverse teams when aggregating the opinions of different agents for architectural
design, and also the effect of having agents optimizing
for different factors of a multi-objective optimization
design problem. We find that a diverse team of agents is
able to provide a higher number of top ranked solutions
for the early stage designer to choose from. Finally, we
present the next steps for a deeper exploration of our
questions.
2014_26_teamcore_waph.pdf Jun-young Kwak, Debarun Kar, William Haskell, Pradeep Varakantham, and Milind Tambe. 2014. “
Building THINC: User Incentivization and Meeting Rescheduling for Energy Savings.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
AbstractThis paper presents THINC, an agent developed for saving energy
in real-world commercial buildings. While previous work has presented techniques for computing energy-efficient schedules, it fails
to address two issues, centered on human users, that are essential in
real-world agent deployments: (i) incentivizing users for their energy saving activities and (ii) interacting with users to reschedule
key “energy-consuming” meetings in a timely fashion, while handling the uncertainty in such interactions. THINC addresses these
shortcomings by providing four new major contributions. First,
THINC computes fair division of credits from energy savings. For
this fair division, THINC provides novel algorithmic advances for
efficient computation of Shapley value. Second, THINC includes a
novel robust algorithm to optimally reschedule identified key meetings addressing user interaction uncertainty. Third, THINC provides an end-to-end integration within a single agent of energy efficient scheduling, rescheduling and credit allocation. Finally, we
deploy THINC in the real-world as a pilot project at one of the main
libraries at the University of Southern California and present results
illustrating the benefits in saving energy.
2014_8_teamcore_aamas14_draft.pdf