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The exponential rise in the number of sensors generating data to the cloud services has boosted studies regarding Internet-of-Things (IoT). In this scenario, a huge problem has arisen: How to accurately deal with this growing demand in processing devices to address such amount of data, and consequently send it to the cloudƒ This paper proposes a fog environment as a solution to deal with this limitation. Therefore, this work presents a set of evaluations that help to understand how to pick IoT sensors up based on its capabilities along with the features required by fog customers applications. In other words, it refers to how to deal with timeliness in Fog Computing Environments. The results showed valuable outcomes based on parameters such as effective transmission and packet error which is essential to explain how to choose IoT sensors when dealing with timeliness in fog computing environments.

In this paper we describe and analyze the use of the Cartesian Genetic Programming method to evolve Artificial Neural Networks (CGPANN) in an open-ended evolution scenario. The issue of open-ended evolution has for some time been considered one of the open problems in the field of Artificial Life. In this paper we analyze the capabilities of CGPANN to evolve behaviors in a scenario without artificial selection, more specifically, without the use of explicit fitness functions. We use the BitBang framework and one of its example scenarios as a proof of concept. The results obtained in these first experiments show that it is indeed possible to evolve CGPANN brains, in an open-ended environment, without any explicit fitness function. We also present an analysis of different parameter configurations for the CGPANN when used in this type of scenario.

A common issue in Artificial Life research, and mainly in open-ended evolution simulations, is that of defining the bootstrap conditions of the simulations. One usual technique employed is the random initialization of individuals at the start of each simulation. However, by using this initialization method, we force the evolutionary process to always start from scratch, and thus require more time to accomplish the objective. Artificial Life simulations, being typically, very time consuming, suffer particularly when applying this method. In a previous paper we described a technique we call step evolution, analogous to incremental evolution techniques, that can be used to shorten the time needed to evolve complex behaviors in open-ended evolutionary simulations. In this paper we further extend this technique by automating the process of stepping the simulation. We provide results from experiments done on an open-ended evolution of foraging scenario, where agents evolve, adapting to a world with a day and night cycle. The results show that we can indeed automate this process and achieve a performance at least as good as on the best performant non-automated version.

A common issue in Artificial Life research, and mainly in open-ended evolution simulations, is that of defining the bootstrap conditions of the simulations. One usual technique employed is the random initialization of individuals at the start of each simulation. However, by using this initialization method, we force the evolutionary process to always start from scratch, and thus require more time to accomplish the objective. Artificial Life simulations, being typically, very time consuming, suffer particularly when applying this method. In this paper, we describe a technique we call step evolution, that can be used to shorten the time needed to evolve complex behaviors in open-ended evolutionary simulations. We provide results from experiments done on an open-ended evolution of foraging scenario, where agents evolve, adapting to a world with a day and night cycle. The results show that the employment of this technique can improve both the overall success of simulation runs, and the time needed to evolve the observed behaviors.

Throughout the last decades, Darwin's theory of natural selection has fueled a vast amount of research in the field of computer science, and more specifically in artificial intelligence. The majority of this work has focussed on artificial selection, rather than on natural selection. In this paper we study the evolution of agents' controllers in an open-ended scenario. To that end, we set up a multi-agent simulation inspired by the ant foraging task, and evolve the agents' brain (a rule list) without any explicit fitness function. We show that the agents do evolve sustainable foraging behaviors in this environment, and discuss some evolutionary conditions that seem to be important to achieve these results.

International audience; We present some methodological lessons and thoughts inferred from a research we are making on a simulation of the Rungis Wholesale Market (in France) using cognitive agents. The implication of using cognitive agents with an objective of realism at the individual level question some of the classical methodological assertions about simulations. Three such lessons are of particular interest: the calibration and validation focus on individuals rather than global values (1); the definition of the simulation model is made independently from the research objectives (2), and without targeting the usual objective of hypothesis simplicity (3).

This paper proposes a new paradigm for the generation of game worlds using principles from the science of complexity. It argues that through the emergence of complex phenomena from simple interactions and building blocks, game worlds capable of surprise and of showing creative behavior may be evolved. A conceptual model incorporating these principles is described and BitBang, a software framework implementing this model, is presented. Usage examples are given and implications for game design are addressed.

This paper describes the implementation of MPI-2 one-sided communications (OSC) for the forthcoming WMPI II product, aimed at clusters of workstations (Windows workstations, for now). This implementation is layered directly on top of the WMPI Management Layer (WML), rather than being on top of the MPI layer and as such can draw more performance from the new features of the WMPI's WML. The major features of this implementation are presented, including the synchronization operations, the remote memory operations and the datatype handling mechanism. Performance benchmarks were taken, comparing the message passing and the one-sided communication models, as well as to compare this implementation with one layered on top of MPI.

This paper presents a dynamic bit frame (window) selection algorithm to address the mismatch in the number of bits in the output of a digital signal processing and the bit range size of the input of the following block. The algorithm is designed to be a flexible tool to tackle general, possibly highly varying input signals in such a way that maintains the maximum signal power without introducing significant data loss. Numerical experiments are presented in the end of the paper to illustrate the behavior of the proposed algorithm.