Systemtheorie Die Systemtheorie ist sowohl eine allgemeine und eigenständige Disziplin als auch ein weitverzweigter und heterogener Rahmen für einen interdisziplinären Diskurs, der den Begriff System als Grundkonzept führt. Es gibt folglich sowohl eine allgemeine „Systemtheorie“ als auch eine Vielzahl unterschiedlicher, zum Teil widersprüchlicher und konkurrierender Systemdefinitionen und -begriffe. Es hat sich heute jedoch eine relativ stabile Reihe an Begriffen und Theoremen herausgebildet, auf die sich der systemtheoretische Diskurs bezieht. Geschichte[Bearbeiten] Der Begriff Allgemeine Systemtheorie geht auf den Biologen Ludwig von Bertalanffy zurück. Kulturgeschichtlich geht der Systembegriff bis auf Johann Heinrich Lambert zurück und wurde unter anderem von Johann Gottfried Herder übernommen und ausgearbeitet. Die moderne Systemtheorie beruht auf unabhängig voneinander entwickelten Ansätzen, die später synthetisiert und erweitert wurden: Der Begriff Systemtheorie bzw. Kybernetik[Bearbeiten]
The SIM_AGENT Package The University of Birmingham School of Computer ScienceThe Cognition and Affect Project Aaron Sloman Slide Presentation on SimAgent Demonstration movies NOTE ON FORMATTING: Adjust the width of your browser window to make the lines of text the length you prefer. This web site does not attempt to impose restrictions on line length or font size. Some External Pointers to the Toolkit This toolkit is referenced at various web sites. The SimAgent toolkit (originally called SIM_AGENT) provides a range of resources for research and teaching related to the development of interacting agents in environments of various degrees and kinds of complexity. That schema accommodates a wide variety of specific architecture types, which differ according to which mechanisms and information structures occur in which boxes, and how they are connected to one another and to the environment, as described in this overview. The OpenPoplog project aims to port the full functionality to Windows+PC. Pop-11 and Poplog
Complex adaptive system They are complex in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities. They are adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events.[1][2] Overview[edit] The term complex adaptive systems, or complexity science, is often used to describe the loosely organized academic field that has grown up around the study of such systems. The fields of CAS and artificial life are closely related. The study of CAS focuses on complex, emergent and macroscopic properties of the system.[3][11][12] John H. General properties[edit] What distinguishes a CAS from a pure multi-agent system (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. Characteristics[edit] Some of the most important characteristics of complex systems are:[14] Robert Axelrod & Michael D.
MASON Multiagent Simulation Toolkit [paper] Keith Sullivan and Sean Luke. 2012. Real-Time Training of Team Soccer Behaviors. In Proceedings of the 2012 RoboCup Workshop. [paper] Keith Sullivan, Katherine Russell, Kevin Andrea, Barak Stout, and Sean Luke. 2012. RoboPatriots: George Mason University 2012 RoboCup Team. [paper] Keith Sullivan and Sean Luke. 2012. [paper] Keith Sullivan, Christopher Vo, and Sean Luke. 2011. [paper] Keith Sullivan and Sean Luke. 2011. [paper] Keith Sullivan, Sean Luke, and Vittorio Ziparo. 2010. [paper] Sean Luke and Vittorio Ziparo. 2010. [paper] Brian Hrolenok, Sean Luke, Keith Sullivan, and Christopher Vo. 2010. [paper] Keith Sullivan, Sean Luke, and Brian Hrolenok. 2010. [paper] Atesmachew Hailegiorgis, William Kennedy, Mark Roleau, Jeffrey Bassett, Mark Coletti, Gabriel Balan, and Tim Gulden. 2010. [paper] William Kennedy, Atesmachew Hailegiorgis, Mark Rouleau, Jeffrey Bassett, Mark Colletti, Gabriel Balan, and Tim Gulden. 2010. [paper] Cioffi-Revilla, Claudio. 2010.
Cellular automaton The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] The red cells are the von Neumann neighborhood for the blue cell, while the extended neighborhood includes the pink cells as well. A torus, a toroidal shape History[edit]
The Wisdom of Crowds The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology. The opening anecdote relates Francis Galton's surprise that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members, and also closer than any of the separate estimates made by cattle experts).[1] Types of crowd wisdom[edit] Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as
Self-organization Self-organization occurs in a variety of physical, chemical, biological, robotic, social and cognitive systems. Common examples include crystallization, the emergence of convection patterns in a liquid heated from below, chemical oscillators, swarming in groups of animals, and the way neural networks learn to recognize complex patterns. Overview[edit] The most robust and unambiguous examples[1] of self-organizing systems are from the physics of non-equilibrium processes. Self-organization is also relevant in chemistry, where it has often been taken as being synonymous with self-assembly. Self-organization usually relies on three basic ingredients:[3] Strong dynamical non-linearity, often though not necessarily involving positive and negative feedbackBalance of exploitation and explorationMultiple interactions Principles of self-organization[edit] History of the idea[edit] Sadi Carnot and Rudolf Clausius discovered the Second Law of Thermodynamics in the 19th century. Developing views[edit]
FEMTO - Cours, simulations, expérimentations Vous êtes étudiant en physique, enseignant ou en passe de l'être dans ce domaine à moins que ce soit en amateur éclairé que vous vous présentez ici ? Bienvenue ! ce site est pour vous. Femto est un projet tourné vers l'enseignement de la physique dans l'enseignement supérieur et propose de nombreuses ressources dans ce domaine. Pour en savoir plus, FEMTO, le projet . Afin de faciliter votre recherche, le site dispose d'une menu horizontal qui permet de trouver des ressources classées par matière et d'un bandeau latéral qui met en lumière les articles récents classés par type. Ca va mieux en le disant Il n'est pas inutile de rappeler que la physique n'est pas seulement une discipline qui s'enseigne tel un dogme. La science recule les frontières et enrichit nos vies, ouvre notre imagination et nous libère des servitudes de l’ignorance et de la superstition.
Encyclopedia of Complexity and Systems Science Assembles for the first time the concepts and tools for analyzing complex systems in a wide range of fields Reflects the real world by integrating complexity with the deterministic equations and concepts that define matter, energy, and the four forces identified in nature Benefits a broad audience: undergraduates, researchers and practitioners in mathematics and many related fields Encyclopedia of Complexity and Systems Science provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. The science and tools of complexity and systems science include theories of self-organization, complex systems, synergetics, dynamical systems, turbulence, catastrophes, instabilities, nonlinearity, stochastic processes, chaos, neural networks, cellular automata, adaptive systems, and genetic algorithms. Content Level » Research Show all authors
Comment devenir un hacker? Pourquoi ce document? En tant qu'éditeur du Jargon File, je reçois souvent des emails d'internautes débutants qui me demandent "comment puis-je apprendre à devenir un hacker?''. Bizarrement, il ne semble pas y avoir de FAQs ou de documents sur le Web qui répondent à cette question vitale. Voici donc ma réponse. Qu'est-ce qu'un hacker? Le Jargon File [traduit en français par Frédéric de SOLLIERS et Christian ROZEBOOM sous le titre Cyberlexis, Editions Masson, NDT] contient un certain nombre de définitions du terme "hacker'', qui sont toutes liées à l'aptitude technique et au plaisir pris à résoudre des problèmes et à dépasser des limites arbitraires. Il existe une communauté, une culture partagée, de programmeurs expérimentés et de spécialistes des réseaux, dont l'histoire remonte aux premiers mini-ordinateurs multi-utilisateurs, il y a quelques dizaines d'années, et aux premières expériences de l'ARPAnet [le réseau connu aujourd'hui sous le nom d'Internet, NDT]. L'attitude des hackers 1.
Percolation threshold Percolation threshold is a mathematical term related to percolation theory , which is the formation of long-range connectivity in random systems. Below the threshold a giant connected component does not exist; while above it, there exists a giant component of the order of system size. In engineering and coffee making , percolation represents the flow of fluids through porous media, but in the mathematics and physics worlds it generally refers to simplified lattice models of random systems or networks (graphs), and the nature of the connectivity in them. [ edit ] Percolation models The most common percolation model is to take a regular lattice, like a square lattice, and make it into a random network by randomly "occupying" sites (vertices) or bonds (edges) with a statistically independent probability p . In the systems described so far, it has been assumed that the occupation of a site or bond is completely random—this is the so-called Bernoulli percolation. [ edit ] 2-Uniform Lattices
The Loginataka: Dialogue between a Guru and a Newbie Translations: Czech Speak, O Guru: How can I become a Unix Wizard? O, Nobly Born: know that the Way to Wizardhood is long, and winding, and Fraught with Risks. Thou must Attune thyself with the Source, attaining the arcane Knowledge and Conversation of the System Libraries and Internals. Speak, O Guru: What books should I study? O, Nobly Born: know that the Nutshell Guides are but the outermost Portal of the True Enlightenment. If thou desirest with True Desire to tread the Path of Wizardly Wisdom, first learn the elementary Postures of Kernighan & Pike's The Unix Programming Environment; then, absorb the mantic puissance of March Rochkind's Advanced Unix Programming and W. Immerse thyself, then, in the Pure Light of Maurice J. For useful hints, tips, and tricks, see Unix Power Tools, Tim O'Reilly, ed. These tomes shall instruct thy Left Brain in the Nature of the Unix System; to Feed the other half of thy Head, O Nobly Born, embrace also the Lore of its Nurture.
Agent-based model An agent-based model (ABM) is one of a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Agent-based models are a kind of microscale model [3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. Most agent-based models are composed of: (1) numerous agents specified at various scales (typically referred to as agent-granularity); (2) decision-making heuristics; (3) learning rules or adaptive processes; (4) an interaction topology; and (5) a non-agent environment. History[edit] Early developments[edit] Theory[edit]
Dessiner d’observation Je ne sais pas si vous avez des photographes professionnels dans votre entourage, ou si vous avez pu observer des experts de la photographie, mais vous est-il déjà arrivé d’apercevoir quelqu’un joindre ses mains de façon bizarre en regardant un paysage ? Peut être que vous vous êtes d’abord demandé s’il sortait tout droit d’un asile psychiatrique ? En fait, il s’agit d’une technique utilisée par les artistes depuis des générations. Elle permet d’obtenir un cadrage rapide, très efficace pour choisir sa composition que ce soit en photographie ou en dessin d’observation. Imaginez que vous faites une superbe balade en forêt ou en ville, et que vous avez décidé de dessiner quelques paysages ou autres scènes visuelles sur votre carnet de croquis. Il existe deux variantes à cette technique : 1/ joignez vos deux mains 2/ ajustez vos mains selon le cadrage que vous choisissez 3/ approchez ou éloignez vos mains de vos yeux selon la taille de votre cadrage et de ce que vous voulez dessiner