Date |
Presenter |
Reading |
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Aug 8 |
Mahsa |
Learning Influence in Computer Social Networks (Franks et al., 2012) |
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Abstract
In open Multi-Agent Systems, where there is no centralised
control and individuals have equal authority, ensuring cooperative
and coordinated behaviour is challenging. Norms
and conventions are a useful means of supporting cooperation
in an emergent decentralised manner, however it takes
time for effective norms and conventions to emerge. Identifying
influential individuals enables the targeted seeding
of desirable norms and conventions, which can reduce the
establishment time and increase efficacy. Existing research
is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location
imbues influence on an agent, and (iii) the extent to which
different network structures affect influence. In this paper,
we propose a general methodology for learning the network
value of a node in terms of influence, and evaluate it using
sampled real-world networks with a model of convention
emergence that has realistic assumptions about the size of
the convention space. We show that (i) the models resulting
from our methodology are effective in predicting influential
network locations, (ii) there are very few locations
that can be classified as influential in typical networks, (iii)
that four single metrics are robustly indicative of influence
across a range of network structures, and (iv) our methodology
learns which single metric or combined measure is the
best predictor of influence in a given network.
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Aug 15 |
No meeting |
Aug 22 |
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Aug 29 |
No meeting |
Sep 5 |
Hamidreza |
Dynamics of Trust Reciprocation in Heterogenous MMOG Networks (Singhal et al., 2013) |
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Abstract
Understanding the dynamics of reciprocation is of
great interest in sociology and computational social science. The
recent growth of Massively Multi-player Online Games (MMOGs)
has provided unprecedented access to large-scale data which
enables us to study such complex human behavior in a more
systematic manner. In this paper, we consider three different
networks in the EverQuest2 game: chat, trade, and trust. The
chat network has the highest level of reciprocation (33%) because
there are essentially no barriers to it. The trade network has
a lower rate of reciprocation (27%) because it has the obvious
barrier of requiring more goods or money for exchange; morever,
there is no clear benefit to returning a trade link except in
terms of social connections. The trust network has the lowest
reciprocation (14%) because this equates to sharing certain
within-game assets such as weapons, and so there is a high
barrier for such connections because they require faith in the
players that are granted such high access. In general, we observe
that reciprocation rate is inversely related to the barrier level in
these networks. We also note that reciprocation has connections
across the heterogeneous networks. Our experiments indicate
that players make use of the medium-barrier reciprocations
to strengthen a relationship. We hypothesize that lower-barrier
interactions are an important component to predicting higherbarrier
ones. We verify our hypothesis using predictive models
for trust reciprocations using features from trade interactions.
Using the number of trades (both before and after the intial trust
link) boosts our ability to predict if the trust will be reciprocated
up to 11% with respect to the AUC. More generally, we see
strong correlations across the different networks and emphasize
that network dynamics, such as reciprocation, cannot be studied
in isolation on just a single type of connection.
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Sep 12 |
Alireza |
The Anatomy of Large Facebook Cascades (Dow et al., 2013) |
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Abstract
When users post photos on Facebook, they have the option
of allowing their friends, followers, or anyone at
all to subsequently reshare the photo. A portion of the
billions of photos posted to Facebook generates cascades
of reshares, enabling many additional users to
see, like, comment, and reshare the photos. In this paper
we present characteristics of such cascades in aggregate,
finding that a small fraction of photos account
for a significant proportion of reshare activity and generate
cascades of non-trivial size and depth. We also
show that the true influence chains in such cascades can
be much deeper than what is visible through direct attribution.
To illuminate how large cascades can form,
we study the diffusion trees of two widely distributed
photos: one posted on President Barack Obama’s page
following his reelection victory, and another posted by
an individual Facebook user hoping to garner enough
likes for a cause.We show that the two cascades, despite
achieving comparable total sizes, are markedly different
in their time evolution, reshare depth distribution, predictability
of subcascade sizes, and the demographics
of users who propagate them. The findings suggest not
only that cascades can achieve considerable size but that
they can do so in distinct ways.
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Sep 19 |
Bennie |
Human Robot Interaction for Multi-Robot Systems (Lewis, 2013) |
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Abstract
The multi-robot manipulation task, where a team of robots cooperatively lift and move objects,
is exceptionally difficult due to the synchronization between the robots. Although increasing the
number of robots can expand the area that the robots can cover within a bounded period of time,
a poor human-robot interface will ultimately compromise the performance of the team of robots.
However, introducing a human operator to the team of robots, does not automatically improve
performance due to the difficulty of teleoperating mobile robots with manipulators. The human
operator’s concentration is divided not only among multiple robots but also between controlling
each robot’s base and arm. This complexity substantially increases the potential neglect time, since
the operator’s inability to effectively attend to each robot during a critical phase of the task leads
to a significant degradation in task performance.
In this dissertation, I propose to develop an agent-based user interface for multi-robot manipulation
interface that identifies and manages any single robot that is not currently being controlled
by the human operator, as well a learning from demonstration technique with the robots learning
from the pre recorded macro representation of the specified task. Propagating the user’s macro
commands from the actively-controlled robot to the neglected robot allows the neglected robot to
leverage this control information and position itself effectively without direct human supervision.
The objective of robot learning from demonstration is to have a robot learn from watching a
demonstration of the task to be performed. A policy is calculated based on the task learned from
demonstrations in which the user verifies that the robot has executed the macro correctly in a new
situation.
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Sep 26 |
Xi |
Learning Collective Behavior in Multi-Relational Networks (Xi, 2013 |
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Abstract
With the rapid expansion of the Internet and WWW, the problem of analyzing social media
data has received an increasing amount of attention in the past decade. The boom in social
media platforms offers many possibilities to study human collective behavior and interactions on
an unprecedented scale. In the past, much work has been done on the problem of learning from
networked data with homogeneous topologies, where instances are explicitly or implicitly interconnected
by a single type of relationship. In contrast to traditional content-only classification
methods, relational learning succeeds in improving classification performance by leveraging the
correlation of the labels between linked instances. However, networked data extracted from social
media, web pages, and bibliographic databases can contain entities of multiple classes and linked
by various causal reasons, hence treating all links in a homogeneous way can limit the performance
of relational classifiers. Learning the collective behavior and interactions in heterogeneous
networks becomes much more complex.
The contribution of this thesis include 1) classification frameworks for identifying human
collective behavior in multi-relational social networks. 2) supervised learning models for relationship
prediction in multi-relational collaborative networks. Our methods improve the performance
of homogeneous relational models by differentiating heterogeneous relations and capturing the
prominent interaction patterns underlying the network structure. The work has been evaluated in
various real-world social networks as well as synthetically generated datasets. We believe that this
study will be useful for analyzing human collective behavior and interactions specifically in the
scenario when the heterogeneous relationships in the network arise from various causal reasons.
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Oct 3 |
Alireza |
The Role of Social Networks in Information Diffusion (Bakshy et al., 2012) |
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Abstract
Online social networking technologies enable individuals to
simultaneously share information with any number of peers.
Quantifying the causal effect of these mediums on the dissemination
of information requires not only identification
of who influences whom, but also of whether individuals
would still propagate information in the absence of social signals
about that information. We examine the role of social
networks in online information diffusion with a large-scale
eld experiment that randomizes exposure to signals about
friends' information sharing among 253 million subjects in
situ. Those who are exposed are significantly more likely to
spread information, and do so sooner than those who are
not exposed. We further examine the relative role of strong
and weak ties in information propagation. We show that,
although stronger ties are individually more influential, it
is the more abundant weak ties who are responsible for the
propagation of novel information. This suggests that weak
ties may play a more dominant role in the dissemination of
information online than currently believed.
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Oct 10 |
Astrid |
Apprenticeship Learning via Inverse Reinforcement Learning (Abbeel and Ng, 2004) Learning for Control from Multiple Demonstrations (Abbeel et al., 2008) |
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Abstract
We consider learning in a Markov decision
process where we are not explicitly given a reward
function, but where instead we can observe
an expert demonstrating the task that
we want to learn to perform. This setting
is useful in applications (such as the task of
driving) where it may be difficult to write
down an explicit reward function specifying
exactly how different desiderata should be
traded o. We think of the expert as trying
to maximize a reward function that is expressible
as a linear combination of known
features, and give an algorithm for learning
the task demonstrated by the expert. Our algorithm
is based on using "inverse reinforcement
learning" to try to recover the unknown
reward function. We show that our algorithm
terminates in a small number of iterations,
and that even though we may never recover
the expert's reward function, the policy output
by the algorithm will attain performance
close to that of the expert, where here performance
is measured with respect to the expert's
unknown reward function.
Abstract
We consider the problem of learning to follow
a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts the—initially unknown—desired trajectory from the sub-optimal expert’s demonstrations and (ii) learns a local model suitable for control along the learned trajectory.
We apply our algorithm to the problem of
autonomous helicopter flight. In all cases,
the autonomous helicopter’s performance exceeds that of our expert helicopter pilot’s
demonstrations. Even stronger, our results
significantly extend the state-of-the-art in autonomous helicopter aerobatics. In particular, our results include the first autonomous
tic-tocs, loops and hurricane, vastly superior
performance on previously performed aerobatic maneuvers (such as in-place flips and
rolls), and a complete airshow, which requires
autonomous transitions between these and
various other maneuvers.
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Oct 17 |
No meeting |
Oct 24 |
Erfan |
Are You Contributing Trustworthy Data? The Case for a
Reputation System in Participatory Sensing (Huang et al., 2010) |
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Abstract
Participatory sensing is a revolutionary new paradigm in
which volunteers collect and share information from their
local environment using mobile phones. The inherent open-
ness of this platform makes it easy to contribute corrupted
data. This paper proposes a novel reputation system that
employs the Gompertz function for computing device rep-
utation score as a re
ection of the trustworthiness of the
contributed data. We implement this system in the con-
text of a participatory noise monitoring application and con-
duct extensive real-world experiments using Apple iPhones.
Experimental results demonstrate that our scheme achieves
three-fold improvement in comparison with the state-of-the-
art Beta reputation scheme.
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Oct 31 |
Bennie |
An Adjustable Autonomy Paradigm for Adapting to Expert-Novice Differences (Lewis et al, 2013) |
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Nov 7 |
Rahmatolla |
Analyzing Agent-based Models using Category Theory (Beheshti and Sukthankar, 2013) |
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Abstract
Agent-based models are a useful technique for
rapidly prototyping complex social systems; they are widely
used in a number of disciplines and can yield theoretical
insights that are different from those produced by a variable
based analysis. However, it remains difficult to compare the
results of two models and to validate the performance of an
agent-based simulation. In this paper, we present a case study
on how to analyze the relationship between agent-based models
using category theory. Category theory is a powerful mathematical
methodology that was originally introduced to organize
mathematical ideas according to their shared structure. It has
been successfully employed in abstract mathematical domains,
but has also enjoyed some success as a formalism for software
engineering. Here we present a procedure for analyzing agent-based
models using category theory and a case study in its
usage at analyzing two different types of simulations.
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Nov 14 |
Erfan |
The Beta Reputation System (Josang and Ishmail, 2002) |
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Abstract
Reputation systems can be used to foster good behaviour and to encourage adherence to contracts in
e-commerce. Several reputation systems have been deployed in practical applications or proposed in the
literature. This paper describes a new system called the beta reputation system which is based on using
beta probability density functions to combine feedback and derive reputation ratings. The advantage of
the beta reputation system is flexibility and simplicity as well as its foundation on the theory of statistics.
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Nov 21 |
Alireza |
Twitter Data Analytics (Chapter 2) (Kumar, Mostatter, Liu, 2013) |
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Nov 28 |
No meeting |
Dec 5 |
Kiran Lakkaraju (Sandia) |
Agent Based Models, Social Phenomena and Massively Multiplayer Online Games |
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