3 research outputs found
Distributed Learning from Interactions in Social Networks
We consider a network scenario in which agents can evaluate each other
according to a score graph that models some interactions. The goal is to design
a distributed protocol, run by the agents, that allows them to learn their
unknown state among a finite set of possible values. We propose a Bayesian
framework in which scores and states are associated to probabilistic events
with unknown parameters and hyperparameters, respectively. We show that each
agent can learn its state by means of a local Bayesian classifier and a
(centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter
that combines plain ML and Empirical Bayes approaches. By using tools from
graphical models, which allow us to gain insight on conditional dependencies of
scores and states, we provide a relaxed probabilistic model that ultimately
leads to a parameter-hyperparameter estimator amenable to distributed
computation. To highlight the appropriateness of the proposed relaxation, we
demonstrate the distributed estimators on a social interaction set-up for user
profiling.Comment: This submission is a shorter work (for conference publication) of a
more comprehensive paper, already submitted as arXiv:1706.04081 (under review
for journal publication). In this short submission only one social set-up is
considered and only one of the relaxed estimators is proposed. Moreover, the
exhaustive analysis, carried out in the longer manuscript, is completely
missing in this versio
Self-Rating in a Community of Peers
International audienceConsider a community of agents, all performing a predefined task, but with different abilities. Each agent may be interested in knowing how well it performs in comparison with her peers. This general scenario is relevant, e.g., in Wireless Sensor Networks (WSNs), or in the context of crowd sensing applications, where devices with embedded sensing capabilities collaboratively collect data to characterize the surrounding environment, but the performance is very sensitive to the accuracy of the gathered measurements. In this paper we present a distributed algorithm allowing each agent to self-rate her level of expertise/performance at the task, as a consequence of pairwise interactions with the peers. The dynamics of the proportions of agents with similar beliefs in their expertise are described using continuous-time state equations. The existence of an equilibrium is shown. Closedform expressions for the various proportions of agents with similar belief in their expertise is provided at equilibrium. Simulation results match well theoretical results in the context of agents equipped with sensors aiming at determining the performance of their sensors