46,552 research outputs found
Distance-Dependent Kronecker Graphs for Modeling Social Networks
This paper focuses on a generalization of stochastic
Kronecker graphs, introducing a Kronecker-like operator and
defining a family of generator matrices H dependent on distances
between nodes in a specified graph embedding. We prove
that any lattice-based network model with sufficiently small
distance-dependent connection probability will have a Poisson
degree distribution and provide a general framework to prove
searchability for such a network. Using this framework, we focus
on a specific example of an expanding hypercube and discuss
the similarities and differences of such a model with recently
proposed network models based on a hidden metric space. We
also prove that a greedy forwarding algorithm can find very short
paths of length O((log log n)^2) on the hypercube with n nodes,
demonstrating that distance-dependent Kronecker graphs can
generate searchable network models
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments
Greedy Forwarding in Dynamic Scale-Free Networks Embedded in Hyperbolic Metric Spaces
We show that complex (scale-free) network topologies naturally emerge from
hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient
greedy forwarding in these networks. Greedy forwarding is topology-oblivious.
Nevertheless, greedy packets find their destinations with 100% probability
following almost optimal shortest paths. This remarkable efficiency sustains
even in highly dynamic networks. Our findings suggest that forwarding
information through complex networks, such as the Internet, is possible without
the overhead of existing routing protocols, and may also find practical
applications in overlay networks for tasks such as application-level routing,
information sharing, and data distribution
MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Relay Selection with Network Coding in Two-Way Relay Channels
In this paper, we consider the design of joint network coding (NC)and relay
selection (RS) in two-way relay channels. In the proposed schemes, two users
first sequentially broadcast their respective information to all the relays. We
propose two RS schemes, a single relay selection with NC and a dual relay
selection with NC. For both schemes, the selected relay(s) perform NC on the
received signals sent from the two users and forward them to both users. The
proposed schemes are analyzed and the exact bit error rate (BER) expressions
are derived and verified through Monte Carlo simulations. It is shown that the
dual relay selection with NC outperforms other considered relay selection
schemes in two-way relay channels. The results also reveal that the proposed NC
relay selection schemes provide a selection gain compared to a NC scheme with
no relay selection, and a network coding gain relative to a conventional relay
selection scheme with no NC.Comment: 11 pages, 5 figure
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