SWARM INTELLIGENCE

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SWARM INTELLIGENCE

• Sumesh Kannan
• Roll No 18

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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.

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Intelligent Agents

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

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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.

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Structure of IA

• Agent Program Architecture
• A Simple Agent Program.

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Simple Reflex Agents

• Follows Condition-Action Rule.
• Needs to perceive its environment completely.

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Model Based Agents

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

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Goal Based Agents

• Makes decisions to achieve a goal.
• More flexible.

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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.

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Environment

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

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An Environment Procedure
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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.

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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.

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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

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Applications

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

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References

• http//en.wikipedia.org
• http//www.swarmbots.com
• http//www.siprojects.com

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• Thank you