12,105 research outputs found
Enabling Social Applications via Decentralized Social Data Management
An unprecedented information wealth produced by online social networks,
further augmented by location/collocation data, is currently fragmented across
different proprietary services. Combined, it can accurately represent the
social world and enable novel socially-aware applications. We present
Prometheus, a socially-aware peer-to-peer service that collects social
information from multiple sources into a multigraph managed in a decentralized
fashion on user-contributed nodes, and exposes it through an interface
implementing non-trivial social inferences while complying with user-defined
access policies. Simulations and experiments on PlanetLab with emulated
application workloads show the system exhibits good end-to-end response time,
low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of
Foundations of Social Computing, ACM Transactions on Internet Technolog
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Many decentralized online social networks (DOSNs) have been proposed due to
an increase in awareness related to privacy and scalability issues in
centralized social networks. Such decentralized networks transfer processing
and storage functionalities from the service providers towards the end users.
DOSNs require individualistic implementation for services, (i.e., search,
information dissemination, storage, and publish/subscribe). However, many of
these services mostly perform social queries, where OSN users are interested in
accessing information of their friends. In our work, we design a socially-aware
distributed hash table (DHTs) for efficient implementation of DOSNs. In
particular, we propose a gossip-based algorithm to place users in a DHT, while
maximizing the social awareness among them. Through a set of experiments, we
show that our approach reduces the lookup latency by almost 30% and improves
the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc
Deep Reinforcement Learning for Swarm Systems
Recently, deep reinforcement learning (RL) methods have been applied
successfully to multi-agent scenarios. Typically, these methods rely on a
concatenation of agent states to represent the information content required for
decentralized decision making. However, concatenation scales poorly to swarm
systems with a large number of homogeneous agents as it does not exploit the
fundamental properties inherent to these systems: (i) the agents in the swarm
are interchangeable and (ii) the exact number of agents in the swarm is
irrelevant. Therefore, we propose a new state representation for deep
multi-agent RL based on mean embeddings of distributions. We treat the agents
as samples of a distribution and use the empirical mean embedding as input for
a decentralized policy. We define different feature spaces of the mean
embedding using histograms, radial basis functions and a neural network learned
end-to-end. We evaluate the representation on two well known problems from the
swarm literature (rendezvous and pursuit evasion), in a globally and locally
observable setup. For the local setup we furthermore introduce simple
communication protocols. Of all approaches, the mean embedding representation
using neural network features enables the richest information exchange between
neighboring agents facilitating the development of more complex collective
strategies.Comment: 31 pages, 12 figures, version 3 (published in JMLR Volume 20
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