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SWARM INTELLIGENCE

SWARM INTELLIGENCE. Sumesh Kannan Roll No 18. Introduction. Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. Introduced by Beni & Wang in 1989.

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SWARM INTELLIGENCE

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  1. SWARM INTELLIGENCE Sumesh Kannan Roll No 18

  2. Introduction • Swarm intelligence (SI) is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. • Introduced by Beni & Wang in 1989. • Typically made up of a population of simple agents. • Examples in nature : ant colonies, bird flocking, animal herding etc.

  3. Intelligent Agents • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.

  4. Rational Agents • Rationality - expected success given what has been perceived. • Rationality is not omniscience. • Ideal rational agent should do whatever action is expected to maximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has. • Factors on which Rationality depends • Performance measure (degree of success). • Percept sequence (everything agent has perceived so far). • Agents knowledge about the environment. • Actions that agent can perform.

  5. Structure of IA • Agent = Program + Architecture • A Simple Agent Program.

  6. Simple Reflex Agents • Follows Condition-Action Rule. • Needs to perceive its environment completely.

  7. Model Based Agents • Need not perceive the environment completely. • Maintains an internal state. • Internal states should be updated.

  8. Goal Based Agents • Makes decisions to achieve a goal. • More flexible.

  9. Utility Based Agents • A complete specification of the utility function allows rational decisions in two kinds of cases. • Many goals, none can be achieved with certainty. • Conflicting goals.

  10. Environment • Accessible vs. Inaccessible • Deterministic vs. Non-deterministic • Episodic vs. Non-episodic • Static vs. Dynamic • Continuous vs. Discreet

  11. An Environment Procedure

  12. Ant Colony Optimization (ACO) • First ACO system- Marco Dorgo,1992 • Ants search for food. • The shorter the path the greater the pheromone left by an ant. • The probability of taking a route is directly proportional to the level of pheromone on that route. • As more and more ants take the shorter path, the pheromone level increases. • Efficiently solves problems like vehicle routing, network maintenance, the traveling salesperson.

  13. Particle Swarm Optimization (PSO) • Population based Stochastic optimization technique. • Developed by Dr. Eberhart & Dr. Kennedy in 1995. • The potential solutions, called particles, fly through the problem space by following the current optimum particles. • Applied in many areas: function optimization, artificial neural network training, fuzzy system control etc.

  14. Swarm Robotics • Most important application area of Swarm Intelligence • Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems • Can accomplish some tasks that would be impossible for a single robot to achieve. • Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, inspection/maintenance, construction, agriculture, and medicine work

  15. Applications • Massive (Multiple Agent Simulation System in Virtual Environment) Software. • Developed Stephen Regelous for visual effects industry. • Snowbots • Developed Sandia National laboratory.

  16. References http://en.wikipedia.org http://www.swarmbots.com http://www.siprojects.com

  17. Thank you

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