Roll No 18
Swarm intelligence (SI) is an artificial intelligence
based around the study of collective behavior in decentralized,
Introduced by Beni & Wang in 1989.
Typically made up of a population of simple agents.
Examples in nature : ant colonies, bird flocking, animal herding
is anything that can be viewed as
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
the agent has.
Factors on which Rationality depends
(degree of success).
(everything agent has perceived so far).
about the environment.
that agent can perform.
Structure of IA
Agent = Program + Architecture
A Simple Agent Program.
Simple Reflex Agents
Needs to perceive its environment completely.
Model Based Agents
Need not perceive the environment completely.
Internal states should be updated.
Goal Based Agents
Makes decisions to achieve a goal.
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.
Accessible vs. Inaccessible
Deterministic vs. Non
Episodic vs. Non
Static vs. Dynamic
Continuous vs. Discreet
An Environment Procedure
Ant Colony Optimization (ACO)
First ACO system
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
Efficiently solves problems like vehicle routing, network
maintenance, the traveling salesperson.
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.
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
Massive (Multiple Agent Simulation System in Virtual
Developed Stephen Regelous for visual effects industry.
Developed Sandia National laboratory.