Adaptive software agents like HEALER have been proposed in the literature recently to recommend intervention plans to homeless shelter officials. However, generating networks for HEALER’s input is challenging. Moreover, HEALER’s solutions are often counter-intuitive to people. This demo paper makes two contributions. First, we demonstrate HEALER’s Facebook application, which parses the Facebook contact lists in order to construct an approximate social network for HEALER. Second, we present a software interface to run human subject experiments (HSE) to understand human biases in recommendation of intervention plans. We plan to use data collected from these HSEs to build an explanation system for HEALER’s solutions.
This paper looks at challenges faced during the ongoing deployment of HEALER, a POMDP based software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. In order to compute its plans, HEALER (i) casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) and constructs social networks of homeless youth at low cost, using a Facebook application. HEALER is currently being deployed in the real world in collaboration with a homeless shelter. Initial feedback from the shelter officials has been positive but they were surprised by the solutions generated by HEALER as these solutions are very counterintuitive. Therefore, there is a need to justify HEALER’s solutions in a way that mirrors the officials’ intuition. In this paper, we report on progress made towards HEALER’s deployment and detail first steps taken to tackle the issue of explaining HEALER’s solutions.
Green security – protection of forests, fish and wildlife – is a critical problem in environmental sustainability. We focus on the problem of optimizing the defense of forests against illegal logging, where often we are faced with the challenge of teaming up many different groups, from national police to forest guards to NGOs, each with differing capabilities and costs. This paper introduces a new, yet fundamental problem: Simultaneous Optimization of Resource Teams and Tactics (SORT). SORT contrasts with most previous game-theoretic research for green security – in particular based on security games – that has solely focused on optimizing patrolling tactics, without consideration of team formation or coordination. We develop new models and scalable algorithms to apply SORT towards illegal logging in large forest areas. We evaluate our methods on a variety of synthetic examples, as well as a real-world case study using data from our on-going collaboration in Madagascar.
Influencing a social network is an important technique, with potential to positively impact society, as we can modify the behavior of a community. For example, we can increase the overall health of a population; Yadav et al. (2015) , for instance, spread information about HIV prevention in homeless populations. However, although influence maximization has been extensively studied [2, 1], their main motivation is viral marketing, and hence they assume that the social network graph is fully known, generally taken from some social media network. However, the graphs recorded in social media do not really represent all the people and all the connections of a population. Most critically, when performing interventions in real life, we deal with large degrees of lack of knowledge. Normally the social agencies have to perform several interviews in order to learn the social network graph . These highly unknown networks, however, are exactly the ones we need to influence in order to have a positive impact in the real world, beyond product advertisement. Additionally, learning a social network graph is very valuable per se. Agencies need data about a population, in order to perform future actions to enhance their well-being, and better actuate in their practices . As mentioned, however, the works in influence maximization are currently ignoring this problem. Each person in a social network actually knows other people, including the ones she cannot directly influence. When we select someone for an intervention (to spread influence), we also have an opportunity to obtain knowledge. Therefore, in this work we present for the first time the problem of simultaneously influencing and mapping a social network. We study the performance of the classical influence maximization algorithm in this context, and show that it can be arbitrarily low. Hence, we study a class of algorithms for this problem, performing an experimentation using four real life networks of homeless populations. We show that our algorithm is competitive with previous approaches in terms of influence, and is significantly better in terms of mapping.
Security agencies including the US Coast Guard, the Federal Air Marshal Service and the Los Angeles Airport police are several major domains that have been deploying Stackelberg security games and related algorithms to protect against a single adversary or multiple, independent adversaries strategically. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender. Given the potential negative effect of these collusive actions, the defender has an incentive to break up collusion by playing off the self-interest of individual adversaries. This paper deals with problem of collusive security games for rational and bounded rational adversaries. The theoretical results verified with human subject experiments showed that behavior model which optimizes against bounded rational adversaries provides demonstrably better performing defender strategies against human subjects.
In this paper, we aim to deter urban crime by recommending optimal police patrol strategies against opportunistic criminals in large scale urban problems. While previous work has tried to learn criminals’ behavior from real world data and generate patrol strategies against opportunistic crimes, it cannot scale up to large-scale urban problems. Our first contribution is a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. In this game abstraction framework, we model the interaction between officers and opportunistic criminals as a game with discrete targets. By merging similar targets, we obtain an abstract game with fewer total targets. We use real world data to learn and plan against opportunistic criminals in this abstract game, and then propagate the results of this abstract game back to the original game. Our second contribution is the layer-generating algorithm used to merge targets as described in the framework above. This algorithm applies a mixed integer linear program (MILP) to merge similar and geographically neighboring targets in the large scale problem. As our third contribution, we propose a planning algorithm that recommends a mixed strategy against opportunistic criminals. Finally, our fourth contribution is a heuristic propagation model to handle the problem of limited data we occasionally encounter in largescale problems. As part of our collaboration with local police departments, we apply our model in two large scale urban problems: a university campus and a city. Our approach provides high prediction accuracy in the real datasets; furthermore, we project significant crime rate reduction using our planning strategy compared to current police strategy.
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER’s sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process. HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable realworld sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application. Finally, (iv) we show hardness results for the problem that HEALER solves. HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the importance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Computational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of security resources. These applications are leading to realworld use-inspired research in the emerging research area of “security games.” The research challenges posed by these applications include scaling up security games to real-world-sized problems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries. In cybersecurity domain, the interaction between the defender and adversary is quite complicated with high degree of incomplete information and uncertainty. While solutions have been proposed for parts of the problem space in cybersecurity, the need of the hour is a comprehensive understanding of the whole space including the interaction with the adversary. We highlight the innovations in security games that could be used to tackle the game problem in cybersecurity.
Aggregating the opinions of different agents is a powerful way to find high-quality solutions to complex problems. However, when using agents in this fashion, there are
two fundamental open questions. First, given a universe of
agents, how to quickly identify which ones should be used
to form a team? Second, given a team of agents, what is the
best way to aggregate their opinions?
Many researchers value diversity when forming teams. LiCalzi and Surucu (2012) and Hong and Page (2004) propose
models where the agents know the utility of the solutions,
and the team converges to the best solution found by one
of its members. Clearly in complex problems the utility of
solutions would not be available, and agents would have to
resort to other methods, such as voting, to take a common
decision. Lamberson and Page (2012) study diversity in the
context of forecasts, where the solutions are represented by
real numbers and the team takes the average of the opinion
of its members. Domains where the possible solutions are
discrete, however, are not captured by such a model.
I proposed a new model to study teams of agents that vote
in discrete solution spaces (Marcolino, Jiang, and Tambe
2013), where I show that a diverse team of weaker agents can
overcome a uniform team made of copies of the best agent.
However, this phenomenon does not always occur, and it is
still necessary to identify when we should use diverse teams
and when uniform teams would be more appropriate.
Hence, in Marcolino et al. (2014b), I shed a new light into
this problem, by presenting a new, more general model of
diversity for teams of voting agents. Using that model I can
predict that diverse teams perform better than uniform teams
in problems with a large action space.
All my predictions are verified in a real system of voting
agents, in the Computer Go domain. I show that: (i) a team
of diverse players gets a higher winning rate than a uniform
team made of copies of the best agent; (ii) the diverse team
plays increasingly better as the board size increases.
Moreover, I also performed an experimental study in the
building design domain. This is a fundamental domain in
the current scenario, since it is known that the design of
a building has a major impact in the consumption of energy throughout its whole lifespan (Lin and Gerber 2014). It
is fundamental to design energy efficient buildings. Meanwhile, it is important to balance other factors, such as construction cost, creating a multi-objective optimization problem. I show that by aggregating the opinions of a team of
agents, a higher number of 1
st ranked solutions in the Pareto
frontier is found than when using a single agent. Moreover,
my approach eliminates falsely reported 1
st ranked solutions
(Marcolino et al. 2014a; 2015).
As mentioned, studying different aggregation rules is also
fundamental. In Jiang et al. (2014), I introduce a novel
method to extract a ranking from agents, based on the frequency that actions are played when sampling them multiple
times. My method leads to significant improvements in the
winning rate in Go games when using the Borda voting rule
to aggregate the generated rankings.
L. S. Marcolino, H. Xu, D. Gerber, B. Kolev, S. Price, E. Pantazis, and M. Tambe. 2015. “Agent Teams for Design Problems .” In International Workshop on Coordination, Organisations, Institutions and Norms (COIN 2015).Abstract
Design imposes a novel social choice problem: using a team
of voting agents, maximize the number of optimal solutions; allowing
a user to then take an aesthetical choice. In an open system of design
agents, team formation is fundamental. We present the first model of
agent teams for design. For maximum applicability, we envision agents
that are queried for a single opinion, and multiple solutions are obtained
by multiple iterations. We show that diverse teams composed of agents
with different preferences maximize the number of optimal solutions,
while uniform teams composed of multiple copies of the best agent are in
general suboptimal. Our experiments study the model in bounded time;
and we also study a real system, where agents vote to design buildings.
Saving energy is a major concern. Hence, it is fundamental to design and construct buildings that are
energy-efficient. It is known that the early stage of architectural design has a significant impact on this matter. However, it is complex to create designs that are
optimally energy efficient, and at the same time balance other essential criterias such as economics, space,
and safety. One state-of-the art approach is to create
parametric designs, and use a genetic algorithm to optimize across different objectives. We further improve
this method, by aggregating the solutions of multiple
agents. We evaluate diverse teams, composed by different agents; and uniform teams, composed by multiple
copies of a single agent. We test our approach across
three design cases of increasing complexity, and show
that the diverse team provides a significantly larger percentage of optimal solutions than single agents.
Stackelberg security games (SSG) have received a significant amount of attention in the literature
for modeling the strategic interactions between a defender and an adversary, in which the defender
has a limited amount of security resources to protect a set of targets from a potential attack by
the adversary. SSGs are at the heart of several significant decision-support applications deployed
in real world security domains. All of these applications rely on standard assumptions made
in SSGs, including that the defender and the adversary each have a single objective which is
to maximize their expected utility. Given the successes and real world impact of previous SSG
research, there is a natural desire to push towards increasingly complex security domains, leading
to a point where considering only a single objective is no longer appropriate.
My thesis focuses on incorporating multiple objectives into SSGs. With multiple conflicting objectives for either the defender or adversary, there is no one solution which maximizes all
objectives simultaneously and tradeoffs between the objectives must be made. Thus, my thesis provides two main contributions by addressing the research challenges raised by considering
SSGs with (1) multiple defender objectives and (2) multiple adversary objectives. These contributions consist of approaches for modeling, calculating, and analyzing the tradeoffs between
objectives in a variety of different settings. First, I consider multiple defender objectives resulting from diverse adversary threats where protecting against each type of threat is treated as a separate objective for the defender. Second, I investigate the defender’s need to balance between the
exploitation of collected data and the exploration of alternative strategies in patrolling domains.
Third, I explore the necessary tradeoff between the efficacy and the efficiency of the defender’s
strategy in screening domains. Forth, I examine multiple adversary objectives for heterogeneous
populations of boundedly rational adversaries that no longer strictly maximize expected utility.
The contributions of my thesis provide the novel game models and algorithmic techniques
required to incorporate multiple objectives into SSGs. My research advances the state of the
art in SSGs and opens up the model to new types of security domains that could not have been
handled previously. As a result, I developed two applications for real world security domains that
either have been or will be tested and evaluated in the field.
. Interdicting the flow of illegal goods (such as drugs and ivory) is a
major security concern for many countries. The massive scale of these networks,
however, forces defenders to make judicious use of their limited resources. While
existing solutions model this problem as a Network Security Game (NSG), they
do not consider humans’ bounded rationality. Previous human behavior modeling
works in Security Games, however, make use of large training datasets that are
unrealistic in real-world situations; the ability to effectively test many models is
constrained by the time-consuming and complex nature of field deployments. In
addition, there is an implicit assumption in these works that a model’s prediction
accuracy strongly correlates with the performance of its corresponding defender
strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In
the following paper, we (1) first demonstrate that predictive reliability is indeed
strong for previous Stackelberg Security Game experiments. We also run our own
set of human subject experiments in such a way that models are restricted to
learning on dataset sizes representative of real-world constraints. In the analysis
on that data, we demonstrate that (2) predictive reliability is extremely weak for
NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set’s exposed attack surface and
Many search and security games played on a graph can
be modeled as normal-form zero-sum games with strategies
consisting of sequences of actions. The size of the strategy
space provides a computational challenge when solving these
games. This complexity is tackled either by using the compact representation of sequential strategies and linear programming, or by incremental strategy generation of iterative double-oracle methods. In this paper, we present novel
hybrid of these two approaches: compact-strategy doubleoracle (CS-DO) algorithm that combines the advantages of
the compact representation with incremental strategy generation. We experimentally compare CS-DO with the standard
approaches and analyze the impact of the size of the support on the performance of the algorithms. Results show that
CS-DO dramatically improves the convergence rate in games
with non-trivial support.
Security agencies in the real world often need to
protect targets with time-dependent values, e.g.,
tourist sites where the number of travelers changes
over time. Since the values of different targets often change asynchronously, the defender can relocate security resources among targets dynamically
to make the best use of limited resources. We propose a game-theoretic scheme to develop dynamic,
randomized security strategies in consideration of
adversary’s surveillance capability. This differs
from previous studies on security games by considering varying target values and continuous strategy spaces of the security agency and the adversary. The main challenge lies in the computational
intensiveness due to the continuous, hence infinite
strategy spaces. We propose an optimal algorithm
and an arbitrarily near-optimal algorithm to compute security strategies under different conditions.
Experimental results show that both algorithms significantly outperform existing approaches.
Recently, there has been an increase of interest in domains involving
repeated interactions between defenders and adversaries. This has been modeled
as a repeated Stackelberg Security Game (repeated SSG). Although different behavioral models have been proposed for the attackers in these games, human subjects experiments for testing these behavioral models in repeated SSGs have not
been conducted previously. This paper presents the first “longitudinal study” – at
least in the context of SSGs – of testing human behavior models in repeated SSG
settings. We provide the following contributions in this paper. First, in order to
test the behavioral models, we design a game that simulates the repeated interactions between the defender and the adversary and deploy it on Amazon Mechanical Turk (AMT). Human subjects are asked to participate in this repeated
task in rounds of the game, with a break between consecutive rounds. Second,
we develop several approaches to keep the human subjects motivated throughout
the course of this longitudinal study so that they participate in all measurement
occasions, thereby minimizing attrition. We provide results showing improvements of retention rate due to implementation of these approaches. Third, we
propose a way of choosing representative payoffs that fit the real-world scenarios
as conducting these experiments are extremely time-consuming and we can only
conduct a limited number of such experiments.
Recently, there has been an increase in interest in applying game
theoretic approaches to domains involving frequent adversary interactions, such as wildlife and fishery protection. In these domains, the law enforcement agency faces adversaries who repeatedly and frequently carry out illegal activities, and thus, do not have
time for extensive surveillance before taking actions. This makes
them significantly different from counter-terrorism domains where
game-theoretic approaches have been widely deployed. This paper
presents a game-theoretic approach to be used by the defender in
these Frequent Adversary Interaction (FAI) domains. We provide
(i) a novel game model for FAI domains, describing the interaction between the defender and the attackers in a repeated game and
(ii) algorithms that plan for the defender strategies to achieve high
average expected utility over all rounds.
This paper presents research on the prototyping of multi-agent systems for architectural design. It proposes a design exploration methodology at the intersection of architecture,
engineering, and computer science. The motivation of the work includes exploring bottom up
generative methods coupled with optimizing performance criteria including for geometric complexity and objective functions for environmental, structural and fabrication parameters. The
paper presents the development of a research framework and initial experiments to provide
design solutions, which simultaneously satisfy complexly coupled and often contradicting
objectives. The prototypical experiments and initial algorithms are described through a set of
different design cases and agents within this framework; for the generation of façade panels for
light control; for emergent design of shell structures; for actual construction of reciprocal
frames; and for robotic fabrication. Initial results include multi-agent derived efficiencies for
environmental and fabrication criteria and discussion of future steps for inclusion of human and
We show that without using any domain knowledge, we can predict
the final performance of a team of voting agents, at any step towards
solving a complex problem. This demo allows users to interact with
our system, and observe its predictions, while playing 9x9 Go.
Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance
in finding the correct answer to complex problems. We present a
novel benefit of voting, that has not been observed before: we can
use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. We present a theoretical explanation of why our prediction
method works. Further, contrary to what would be expected based
on a simpler explanation using classical voting models, we argue
that we can make accurate predictions irrespective of the strength
(i.e., performance) of the teams, and that in fact, the prediction can
work better for diverse teams composed of different agents than
uniform teams made of copies of the best agent. We perform experiments in the Computer Go domain, where we obtain a high
accuracy in predicting the final outcome of the games. We analyze
the prediction accuracy for three different teams with different levels of diversity and strength, and we show that the prediction works
significantly better for a diverse team. Since our approach is domain independent, it can be easily applied to a variety of domains.