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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

SWARM INTELLIGENCE

Sumesh Kannan

Roll No 18

introduction
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.
intelligent agents
Intelligent Agents
  • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.
rational agents
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.
structure of ia
Structure of IA
  • Agent = Program + Architecture
  • A Simple Agent Program.
simple reflex agents
Simple Reflex Agents
  • Follows Condition-Action Rule.
  • Needs to perceive its environment completely.
model based agents
Model Based Agents
  • Need not perceive the environment completely.
  • Maintains an internal state.
  • Internal states should be updated.
goal based agents
Goal Based Agents
  • Makes decisions to achieve a goal.
  • More flexible.
utility based agents
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.
environment
Environment
  • Accessible vs. Inaccessible
  • Deterministic vs. Non-deterministic
  • Episodic vs. Non-episodic
  • Static vs. Dynamic
  • Continuous vs. Discreet
ant colony optimization aco
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.
particle swarm optimization pso
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.
swarm robotics
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
applications
Applications
  • Massive (Multiple Agent Simulation System in Virtual Environment) Software.
    • Developed Stephen Regelous for visual effects industry.
  • Snowbots
    • Developed Sandia National laboratory.
references
References

http://en.wikipedia.org

http://www.swarmbots.com

http://www.siprojects.com