Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Modèle comportemental de la dynamique de construction de la structure épigée du nid chez la fourmi Lasius niger : approches expérimentales et théoriques

La structure épigée du nid de la fourmi Lasius niger, prise comme un exemple typique des structures alvéolaires construites par les insectes sociaux, résulte de l'accumulation d'actes individuels de prise, de transport et de dépôt de matériau. Nous montrons expérimentalement que ces structures émergent d'une coordination indirecte des actes de prise/dépôt par l'activité précédente : les dépôts sont plus fréquents dans les zones de forte densité, et les prises dans les zones de faible densité. Il s'agit donc d'une dynamique auto-organisée où des boucles de rétroaction amplifient des premiers dépôts aléatoires. Au cours du temps, la surface de la structure devient alvéolaire, et le déplacement des fourmis peut être affecté par ses déclivités et ses courbures. A ce stade, le processus de construction présente donc un double couplage de la structure avec d'une part les décisions comportementales de prise et de dépôt, et d'autre part le déplacement des fourmis. Pour ce dernier, nous proposons le modèle du Marcheur de Boltzmann généralisé qui intègre ces effets d'orientation par la structure. Nous proposons enfin une formulation intégrale des échanges de matériau entre points du système, qui intègre tous ces éléments. Ce formalisme confirme le critère d'émergence obtenu par l'analyse linéaire de stabilité classique sur la phase initiale et permet de comprendre les mécanismes essentiels de cette dynamique, en lien direct avec la représentation du phénomène en termes de comportements individuels.Epigenous part of the nest in the ant Lasius niger is a typical example of sponge-like structures built by social insects. It results from accumulated tiny pellets of material which are picked up, moved and dropped by individuals. We show experimentally that these structured patterns emerge from a coordination of individual decisions mediated by the evolving density of material: they pick up more often in depleted zones and drop preferentially in high-density zones. This self-organized process allows random fluctuations in early material density field to be amplified through time. The building then evolves towards a sponge-like structure, so that the surface displays slopes and curvatures, which might in turn affect motion decisions. In late stage, the coupling between density and behavioral decisions is then intricated with coupling between geometry and motion. For the latter, we designed the Generalized Boltzmann Walker model so as to integrate local geometry with random walk. We demonstrate experimentally its relevance for the effect of slopes. Eventually, we analyze the whole process within an analysis of net exchange of material between pairs of locations. We confirm then some results obtained by classical linear stability analysis, and explain essential properties of those dynamics in terms of measured individual behaviors and cognitive properties

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains