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Multi-agent system

Multi-agent system
Simple reflex agent Learning agent Concept[edit] Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots,[5] humans or human teams. Agents can be divided into different types: Very simple like: passive agents[6] or agent without goals (like obstacle, apple or key in any simple simulation)Active agents[6] with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)Or very complex agents (like cognitive agent, which contain complex calculations) Environment also can be divided into: Virtual EnvironmentDiscrete EnvironmentContinuous Environment Characteristics[edit] The agents in a multi-agent system have several important characteristics:[10] Self-organization and self-steering[edit] Systems paradigms[edit] Many M.A. systems are implemented in computer simulations, stepping the system through discrete "time steps". First a "Who can?"

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Systems thinking Impression of systems thinking about society[1] A system is composed of interrelated parts or components (structures) that cooperate in processes (behavior). Natural systems include biological entities, ocean currents, the climate, the solar system and ecosystems. Designed systems include airplanes, software systems, technologies and machines of all kinds, government agencies and business systems. Systems Thinking has at least some roots in the General System Theory that was advanced by Ludwig von Bertalanffy in the 1940s and furthered by Ross Ashby in the 1950s. Complex systems Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment.[1] Such systems are used to model processes in computer science, biology,[2] economics, physics, chemistry,[3] and many other fields. It is also called complex systems theory, complexity science, study of complex systems, sciences of complexity, non-equilibrium physics, and historical physics. A variety of abstract theoretical complex systems is studied as a field of mathematics. The key problems of complex systems are difficulties with their formal modelling and simulation.

Ants Colony and Multi-Agents Ants Colony and Multi-Agents Individual ants are simple insects with limited memory and capable of performing simple actions. However, an ant colony expresses a complex collective behavior providing intelligent solutions to problems such as carrying large items, forming bridges and finding the shortest routes from the nest to a food source. A single ant has no global knowledge about the task it is performing. The ant's actions are based on local decisions and are usually unpredictable. The intelligent behavior naturally emerges as a consequence of the self-organization and indirect communication between the ants. Ambient intelligence An (expected) evolution of computing from 1960–2010. In computing, ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. Ambient intelligence is a vision on the future of consumer electronics, telecommunications and computing that was originally developed in the late 1990s for the time frame 2010–2020. In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices (see Internet of Things). As these devices grow smaller, more connected and more integrated into our environment, the technology disappears into our surroundings until only the user interface remains perceivable by users. A typical context of ambient intelligence environment is a Home environment (Bieliková & Krajcovic 2001).

The breve Simulation Environment What is breve? breve is a free, open-source software package which makes it easy to build 3D simulations of multi-agent systems and artificial life. Using Python, or using a simple scripting language called steve, you can define the behaviors of agents in a 3D world and observe how they interact. breve includes physical simulation and collision detection so you can simulate realistic creatures, and an OpenGL display engine so you can visualize your simulated worlds. Ant colony optimization algorithms Ant behavior was the inspiration for the metaheuristic optimization technique This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. Overview[edit] Summary[edit]

ECJ ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. Emergent Intelligence in Competitive Multi-Agent Systems by Sander M. Bohte, Han La Poutré Getting systems with many independent participants to behave is a great challenge. g factor (psychometrics) The g factor (short for "general factor") is a construct developed in psychometric investigations of cognitive abilities. It is a variable that summarizes positive correlations among different cognitive tasks, reflecting the fact that an individual's performance at one type of cognitive task tends to be comparable to his or her performance at other kinds of cognitive tasks. The g factor typically accounts for 40 to 50 percent of the between-individual variance in IQ test performance, and IQ scores are frequently regarded as estimates of individuals' standing on the g factor.[1] The terms IQ, general intelligence, general cognitive ability, general mental ability, or simply intelligence are often used interchangeably to refer to the common core shared by cognitive tests.[2]

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. Intelligence as an Emergent Behavior or, The Songs of Eden Published on Monday, March 16, 01998 • 16 years, 1 month ago Written by Danny Hillis for Daedalus Sometimes a system with many simple components will exhibit a behavior of the whole that seems more organized than the behavior of the individual parts. Consider the intricate structure of a snowflake. Symmetric shapes within the crystals of ice repeat in threes and sixes, with patterns recurring from place to place and within themselves at different scales. The shapes formed by the ice are consequences of the local rules of interaction that govern the molecules of water, although the connection between the shapes and the rules is far from obvious.

Fluid and crystallized intelligence Fluid intelligence or fluid reasoning is the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. It is the ability to analyze novel problems, identify patterns and relationships that underpin these problems and the extrapolation of these using logic. It is necessary for all logical problem solving, e.g., in scientific, mathematical, and technical problem solving. Fluid reasoning includes inductive reasoning and deductive reasoning.