Naming Game on Adaptive Weighted Networks

We examine a naming game on an adaptive weighted network. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. In some cases, depending on the parameters of the model, the preference toward successfully communicating agents is basically negligible and the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. In some other cases, the preference toward successfully communicating agents becomes much more relevant and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still being present. A comparison of distribution of languages in our model and in the human population is discussed.Comment: 22 pages, accepted in Artificial Lif

Distributed privacy-preserving network size computation: A system-identification based method

In this study, we propose an algorithm for computing the network size of communicating agents. The algorithm is distributed: a) it does not require a leader selection; b) it only requires local exchange of information, and; c) its design can be implemented using local information only, without any global information about the network. It is privacy-preserving, namely it does not require to propagate identifying labels. This algorithm is based on system identification, and more precisely on the identification of the order of a suitably-constructed discrete-time linear time-invariant system over some finite field. We provide a probabilistic guarantee for any randomly picked node to correctly compute the number of nodes in the network. Moreover, numerical implementation has been taken into account to make the algorithm applicable to networks of hundreds of nodes, and therefore make the algorithm applicable in real-world sensor or robotic networks. We finally illustrate our results in simulation and conclude the paper with discussions on how our technique differs from a previously-known strategy based on statistical inference.Comment: 52nd IEEE Conference on Decision and Control (CDC 2013) (2013

Decentralized Collaborative Learning of Personalized Models over Networks

We consider a set of learning agents in a col-laborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar objectives? We introduce and analyze two asynchronous gossip algorithms running in a fully decentralized manner. Our first approach , inspired from label propagation, aims to smooth pre-trained local models over the network while accounting for the confidence that each agent has in its initial model. In our second approach, agents jointly learn and propagate their model by making iterative updates based on both their local dataset and the behavior of their neighbors. Our algorithm to optimize this challenging objective in a decentralized way is based on ADMM

GRA W NAZYWANIE Z PREFERENCYJNYM WYBOREM PARTNERÓW

In this paper, a naming game on an adaptive weighted network is examined. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. When the preference toward successfully communicating agents is not so strong, the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. Much different behaviour appears when the preference toward successfully communicating agents is stronger and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still being present. A comparison of distribution of languages in the model and in the human population is discussed.In this paper, a naming game on an adaptive weighted network is examined. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. When the preference toward successfully communicating agents is not so strong, the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. Much different behaviour appears when the preference toward successfully communicating agents is stronger and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still being present. A comparison of distribution of languages in the model and in the human population is discussed.

A Java-based Mobile Agent Framework for Distributed Network Applications

Recently, a new paradigm has emerged forstructuring and developing distributed network applications inopen distributed and heterogeneous environments. Manyapplication areas, such as electronic commerce, mobilecomputing, network management and information retrieval canbenefit from the application of the Mobile Agent technology. Theexploitation of Mobile Agents offers several peculiar advantages,such as reduction of network latency, asynchronous execution,robust and fault tolerant behavior. Java technology provides aplatform-independent, portable software environment whichmakes it an excellent tool for mobile agent development. MobileAgents are mainly intended to be used for applicationsdistributed over large scale (slow) networks because they allowsaving communication costs by moving computation to the hoston which the target data resides. However, it has not becomepopular due to some problems such as security. In this paper, wepresent a distributed network architecture based on the MobileAgent approach. A network of communicating servers each ofwhich support multiple clients is our goal. We also propose asecurity approach for mobile agents, which protect critical dataof mobile agents from malicious attacks, by using cryptographictechniques. We implement a bank service application to be testedon our mobile agent framework. The results suggest that fornetworks with high latency, Mobile Agents may provideimprovements over more conventional client-server systems

Active Coordination in Ad Hoc Networks

The increasing ubiquity of communicating mobile devices and vastly different mobile application needs have led to the emergence of middleware models for ad hoc networks that simplify application programming. One such system, EgoSpaces, addresses specific needs of individual applications, allowing them to define what data is included in their operating context using declarative specifications constraining properties of data, agents that own the data, hosts on which those agents are running, and attributes of the ad hoc network. In the resulting coordination model, application agents interact with a dynamically changing environment through a set of views, or custom defined projections of the set of data present in the surrounding ad hoc network. This paper builds on EgoSpaces by allowing agents to assign behaviors to their personal-ized views. Behaviors consist of actions that are automatically performed in response to specified changes in a view. Behaviors discussed in this paper encompass reactive programming, transparent data migration, automatic data duplication, and event capture. Formal semantic definitions and programming examples are given for each behavior