20,171 research outputs found
Interactive Sensing in Social Networks
This paper presents models and algorithms for interactive sensing in social
networks where individuals act as sensors and the information exchange between
individuals is exploited to optimize sensing. Social learning is used to model
the interaction between individuals that aim to estimate an underlying state of
nature. In this context the following questions are addressed: How can
self-interested agents that interact via social learning achieve a tradeoff
between individual privacy and reputation of the social group? How can
protocols be designed to prevent data incest in online reputation blogs where
individuals make recommendations? How can sensing by individuals that interact
with each other be used by a global decision maker to detect changes in the
underlying state of nature? When individual agents possess limited sensing,
computation and communication capabilities, can a network of agents achieve
sophisticated global behavior? Social and game theoretic learning are natural
settings for addressing these questions. This article presents an overview,
insights and discussion of social learning models in the context of data incest
propagation, change detection and coordination of decision making
Distributed Learning for Cooperative Inference
We study the problem of cooperative inference where a group of agents
interact over a network and seek to estimate a joint parameter that best
explains a set of observations. Agents do not know the network topology or the
observations of other agents. We explore a variational interpretation of the
Bayesian posterior density, and its relation to the stochastic mirror descent
algorithm, to propose a new distributed learning algorithm. We show that, under
appropriate assumptions, the beliefs generated by the proposed algorithm
concentrate around the true parameter exponentially fast. We provide explicit
non-asymptotic bounds for the convergence rate. Moreover, we develop explicit
and computationally efficient algorithms for observation models belonging to
exponential families
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures
Scalable communication is of utmost importance for reliable dissemination of
time-sensitive information in cooperative vehicular ad-hoc networks (VANETs),
which is, in turn, an essential prerequisite for the proper operation of the
critical cooperative safety applications. The model-based communication (MBC)
is a recently-explored scalability solution proposed in the literature, which
has shown a promising potential to reduce the channel congestion to a great
extent. In this work, based on the MBC notion, a technology-agnostic hybrid
model selection policy for Vehicle-to-Everything (V2X) communication is
proposed which benefits from the characteristics of the non-parametric Bayesian
inference techniques, specifically Gaussian Processes. The results show the
effectiveness of the proposed communication architecture on both reducing the
required message exchange rate and increasing the remote agent tracking
precision.Comment: Accepted for Oral Presentation at the 13th IEEE Systems Conference
(SysCon 2019
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
The cooperative hierarchical structure is a common and significant data
structure observed in, or adopted by, many research areas, such as: text mining
(author-paper-word) and multi-label classification (label-instance-feature).
Renowned Bayesian approaches for cooperative hierarchical structure modeling
are mostly based on topic models. However, these approaches suffer from a
serious issue in that the number of hidden topics/factors needs to be fixed in
advance and an inappropriate number may lead to overfitting or underfitting.
One elegant way to resolve this issue is Bayesian nonparametric learning, but
existing work in this area still cannot be applied to cooperative hierarchical
structure modeling.
In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP)
to fill this gap. Each node in a cooperative hierarchical structure is assigned
a Dirichlet process to model its weights on the infinite hidden factors/topics.
Together with measure inheritance from hierarchical Dirichlet process, two
kinds of measure cooperation, i.e., superposition and maximization, are defined
to capture the many-to-many relationships in the cooperative hierarchical
structure. Furthermore, two constructive representations for CHDP, i.e.,
stick-breaking and international restaurant process, are designed to facilitate
the model inference. Experiments on synthetic and real-world data with
cooperative hierarchical structures demonstrate the properties and the ability
of CHDP for cooperative hierarchical structure modeling and its potential for
practical application scenarios
Location-Based Reasoning about Complex Multi-Agent Behavior
Recent research has shown that surprisingly rich models of human activity can
be learned from GPS (positional) data. However, most effort to date has
concentrated on modeling single individuals or statistical properties of groups
of people. Moreover, prior work focused solely on modeling actual successful
executions (and not failed or attempted executions) of the activities of
interest. We, in contrast, take on the task of understanding human
interactions, attempted interactions, and intentions from noisy sensor data in
a fully relational multi-agent setting. We use a real-world game of capture the
flag to illustrate our approach in a well-defined domain that involves many
distinct cooperative and competitive joint activities. We model the domain
using Markov logic, a statistical-relational language, and learn a theory that
jointly denoises the data and infers occurrences of high-level activities, such
as a player capturing an enemy. Our unified model combines constraints imposed
by the geometry of the game area, the motion model of the players, and by the
rules and dynamics of the game in a probabilistically and logically sound
fashion. We show that while it may be impossible to directly detect a
multi-agent activity due to sensor noise or malfunction, the occurrence of the
activity can still be inferred by considering both its impact on the future
behaviors of the people involved as well as the events that could have preceded
it. Further, we show that given a model of successfully performed multi-agent
activities, along with a set of examples of failed attempts at the same
activities, our system automatically learns an augmented model that is capable
of recognizing success and failure, as well as goals of peoples actions with
high accuracy. We compare our approach with other alternatives and show that
our unified model, which takes into account not only relationships among
individual players, but also relationships among activities over the entire
length of a game, although more computationally costly, is significantly more
accurate. Finally, we demonstrate that explicitly modeling unsuccessful
attempts boosts performance on other important recognition tasks
Distributed Detection via Bayesian Updates and Consensus
In this paper, we discuss a class of distributed detection algorithms which
can be viewed as implementations of Bayes' law in distributed settings. Some of
the algorithms are proposed in the literature most recently, and others are
first developed in this paper. The common feature of these algorithms is that
they all combine (i) certain kinds of consensus protocols with (ii) Bayesian
updates. They are different mainly in the aspect of the type of consensus
protocol and the order of the two operations. After discussing their
similarities and differences, we compare these distributed algorithms by
numerical examples. We focus on the rate at which these algorithms detect the
underlying true state of an object. We find that (a) The algorithms with
consensus via geometric average is more efficient than that via arithmetic
average; (b) The order of consensus aggregation and Bayesian update does not
apparently influence the performance of the algorithms; (c) The existence of
communication delay dramatically slows down the rate of convergence; (d) More
communication between agents with different signal structures improves the rate
of convergence.Comment: 6 pages, 3 figures. This paper has been submitted to Chinese Control
Conference 2015 at Hangzhou, People's Republic of Chin
Intelligence and Cooperative Search by Coupled Local Minimizers
We show how coupling of local optimization processes can lead to better
solutions than multi-start local optimization consisting of independent runs.
This is achieved by minimizing the average energy cost of the ensemble, subject
to synchronization constraints between the state vectors of the individual
local minimizers. From an augmented Lagrangian which incorporates the
synchronization constraints both as soft and hard constraints, a network is
derived wherein the local minimizers interact and exchange information through
the synchronization constraints. From the viewpoint of neural networks, the
array can be considered as a Lagrange programming network for continuous
optimization and as a cellular neural network (CNN). The penalty weights
associated with the soft state synchronization constraints follow from the
solution to a linear program. This expresses that the energy cost of the
ensemble should maximally decrease. In this way successful local minimizers can
implicitly impose their state to the others through a mechanism of master-slave
dynamics resulting into a cooperative search mechanism. Improved information
spreading within the ensemble is obtained by applying the concept of
small-world networks. This work suggests, in an interdisciplinary context, the
importance of information exchange and state synchronization within ensembles,
towards issues as evolution, collective behaviour, optimality and intelligence.Comment: 25 pages, 10 figure
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Decentralized Bayesian Learning over Graphs
We propose a decentralized learning algorithm over a general social network.
The algorithm leaves the training data distributed on the mobile devices while
utilizing a peer to peer model aggregation method. The proposed algorithm
allows agents with local data to learn a shared model explaining the global
training data in a decentralized fashion. The proposed algorithm can be viewed
as a Bayesian and peer-to-peer variant of federated learning in which each
agent keeps a "posterior probability distribution" over a global model
parameters. The agent update its "posterior" based on 1) the local training
data and 2) the asynchronous communication and model aggregation with their
1-hop neighbors. This Bayesian formulation allows for a systematic treatment of
model aggregation over any arbitrary connected graph. Furthermore, it provides
strong analytic guarantees on converge in the realizable case as well as a
closed form characterization of the rate of convergence. We also show that our
methodology can be combined with efficient Bayesian inference techniques to
train Bayesian neural networks in a decentralized manner. By empirical studies
we show that our theoretical analysis can guide the design of network/social
interactions and data partitioning to achieve convergence
Dependency Networks for Collaborative Filtering and Data Visualization
We describe a graphical model for probabilistic relationships---an
alternative to the Bayesian network---called a dependency network. The graph of
a dependency network, unlike a Bayesian network, is potentially cyclic. The
probability component of a dependency network, like a Bayesian network, is a
set of conditional distributions, one for each node given its parents. We
identify several basic properties of this representation and describe a
computationally efficient procedure for learning the graph and probability
components from data. We describe the application of this representation to
probabilistic inference, collaborative filtering (the task of predicting
preferences), and the visualization of acausal predictive relationships.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
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