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In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally(More)
The control of robot swarming in a distributed manner is a difficult problem because global behaviors must emerge as a result of many local actions. This paper uses a bio-inspired control method called the Digital Hormone Model (DHM) to control the tasking and executing of robot swarms based on local communication, signal propagation, and stochastic(More)
We consider the problem of developing data-driven proba-bilistic models describing the activity profile of users in on-line social network settings. Previous models of user activities have discarded the potential influence of a user's network structure on his temporal activity patterns. Here we address this shortcoming and suggest an alternative approach(More)
We describe a novel method for node localization in a sensor network where there are a fraction of reference nodes with known locations. For application-specific sensor networks, we argue that it makes sense to treat localization through online distributed learning and integrate it with an application task such as target tracking. We propose distributed(More)
In this paper, we review methods used for macroscopic mod-eling and analyzing collective behavior of swarm robotic systems. Although the behavior of an individual robot in a swarm is often characterized by an important stochastic component, the collective behavior of swarms is statistically predictable and has often a simple probabilis-tic description.(More)
Traditionally, two alternative design design approaches have been available to engineers: top-down and bottom-up. In the top-down approach , the design process starts with specifying the global system state and assuming that each component has global knowledge of the system, as in a centralized approach. The solution is then decentralized by replacing(More)
One of the main challenges in Grid computing is efficient allocation of resources (CPU Y hours, network bandwidth, etc.) to the tasks submitted by users. Due to the lack of centralized control and the dynamic/ stochastic nature of resource availability, any successful allocation mechanism should be highly distributed and robust to the changes in the Grid(More)
In this paper we study a class of resource allocation games which are inspired by the El Farol Bar problem. We consider a system of competitive agents that have to choose between several resources characterized by their time dependent capacities. The agents using a particular resource are rewarded if their number does not exceed the resource capacity, and(More)