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SWARM INTELLIGENCE

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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 ... – PowerPoint PPT presentation

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Title: SWARM INTELLIGENCE


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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