We study distributed big-data nonconvex optimization in multiagent networks. consider the (constrained) minimization of sum a smooth (possibly) function, i.e., agents' sum-utility, plus convex nonsmooth regularizer. Our interest is on problems which there large number variables to optimize. If treated by means standard algorithms, these large-scale may be intractable due prohibitive local compu...

Searching and tracking are important issues in multi-robot observation of multiple moving targets. In this paper, our distributed memory based searching and artificial potential field based tracking algorithms are presented. For searching, a memory table, either local or shared, helps to find targets. For tracking, an artificial potential field based motion control algorithm provides real-time ...

This paper addresses distributed multi-object tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The main contribution is an approach to distributed multi-object estimation based on labeled Random Finite Sets (RFSs) and dynamic Bayesian inference, which enables the development of two novel consensus tracking filte...

Free/Libre Open Source Software (FLOSS) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards. FLOSS development teams some how profit from the advantages and evade the challenges of distributed software development. Despite the relevance of the FLOSS both for research and p...

Programming languages are not only useful to command computers, they also increasingly are a medium for human communication. I will use the framework of distributed cognition to discuss how knowledge is shared in a team of programmers and to show that computer code plays an important role in it. The resulting model of how programmers comprehend code suggests that common grounds play an importan...