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?"
Travelling salesman problem The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science. Solution of a travelling salesman problem TSP is a special case of the travelling purchaser problem. In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. History[edit] The origins of the travelling salesman problem are unclear. Richard M.
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. There are many other examples of Emergent Behavior in nature such as colonies of bacteria, bees and so on. The fascinating behavior of ants has been inspiring researches to create new approaches based on some of the abilities of the ants' colonies. The practical example covered in this essay involves finding a path linking two nodes in a graph. Foraging behavior of ants Ants use a signaling communication system based on the deposition of pheromone over the path it follows, marking a trail. Environment
Knapsack problem Example of a one-dimensional (constraint) knapsack problem: which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg? A multiple constrained problem could consider both the weight and volume of the boxes. (Answer: if any number of each box is available, then three yellow boxes and three grey boxes; if only the shown boxes are available, then all but the green box.) The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a mass and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. Applications[edit] Definition[edit] Mathematically the 0-1-knapsack problem can be formulated as: Let there be items, to
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. At CWI, the Computational Intelligence and Multi- Agent Games research group applies principles from both the economic field of mechanism design and state-of-the-art machine-learning techniques to develop systems in which 'proper' behaviour emerges from the selfish actions of their components. With the rapid transition of the real economy to electronic interactions and markets, applications are numerous: from automatic negotiation of bundles of personalized news, to efficient routing of trucks or targeted advertisement. In an economic setting, an individual - or agent - is assumed to behave selfishly: agents compete with each other to acquire the most resources (utility) from their interactions. Clearly, software agents in a multi-agent system must be intelligent and adaptive. In a similar vein, we considered the dynamic scheduling of trucking routes and freight.
Ant robotics Ant robotics is a special case of swarm robotics. Swarm robots are simple (and hopefully, therefore cheap) robots with limited sensing and computational capabilities. This makes it feasible to deploy teams of swarm robots and take advantage of the resulting fault tolerance and parallelism. Swarm robots cannot use conventional planning methods due to their limited sensing and computational capabilities. Thus, their behavior is often driven by local interactions. Ant robots are swarm robots that can communicate via markings, similar to ants that lay and follow pheromone trails. Invention[edit] In 1991, American electrical engineer James McLurkin was the first to conceptualize the idea of "robot ants" while working at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Background[edit] See also[edit] References[edit] ^ Jump up to: a b J. External links[edit] Ant robot by Sven KoenigAnt algorithm by Israel Wagner
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. It would be very convenient if intelligence were an emergent behavior of randomly connected neurons in the same sense that snowflakes and whirlpools are the emergent behaviors of water molecules. This is a seductive idea, since it allows for the possibility of constructing intelligence without first understanding it. There has been a renewal of interest in emergent behavior in the form of neural networks and connectionist models, spin glasses and cellular automata, and evolutionary models.
Emergent Intelligence of Networked Agents Contains the latest research on Emergent Intelligence of Networked Agents The study of intelligence emerged from interactions among agents has been popular. In this study it is recognized that a network structure of the agents plays an important role. The current state-of-the art in agent-based modeling tends to be a mass of agents that have a series of states that they can express as a result of the network structure in which they are embedded. This book is based on communications given at the Workshop on Emergent Intelligence of Networked Agents (WEIN 06) at the Fifth International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2006), which was held at Future University, Hakodate, Japan, from May 8 to 12, 2006. Content Level » Research Keywords » Emergent Intelligence - Networked Agents Related subjects » Artificial Intelligence - Computational Intelligence and Complexity Table of contents / Preface Popular Content within this publication Show all authors Hide authors
Luca Gambardella: Ant Colony Optimization by Luca Maria Gambardella and Marco Dorigo Ant Colony Optimization: ants inspired systems for combinatorial optimization The ant colony optimization metaheuristic (ACO, Dorigo, Di Caro and Gambardella 1999) is a population-based approach to the solution of combinatorial optimization problems. ACO: Ant Colony Optimization Dorigo M., G.
Artificial Ants for Optimization: Breaking Benchmark Records / Swarm Intelligence Artificial Ants (AAs) communicate via artificial pheromones that evaporate over time. To solve complex optimization problems, Schmidhuber's fellow co-director Luca Maria Gambardella (IDSIA) and Marco Dorigo (ex-IDSIA, now professor in Belgium) introduced AAs equipped with local search methods. This approach broke several important benchmark world records, including those for sequential ordering problems and routing problems (essential for vehicle routing, internet routing, routing in ad-hoc networks, and so on). Check out some of the numerous citations at Google Scholar. This success led to a recent IDSIA spin-off company called ANTOPTIMA. Commercial applications include vehicle routing, logistics for the largest Mediterranean container terminal in LaSpezia, and truck fleet management (the largest Swiss retailer is now using AAs to distribute its goods).
Scientific workflow system A scientific workflow system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a scientific application. A specialized form of scientific workflow systems is a bioinformatics workflow management system which focuses on a specific domain of science, bioinformatics. The rising interest in scientific workflow systems has coincided with rising interest in e-Science technologies and applications, and in grid computing. The vision of e-Science is that of distributed scientists being able to collaborate on conducting large scale scientific experiments and knowledge discovery applications using distributed systems of computing resources, data sets, and devices. There are many motives for differentiating scientific workflows from traditional business process workflows. Scientific workflows[edit] More specialized scientific workflow systems, e.g. Examples[edit] See also[edit]