lecture 7 aim

8/18/2019 Lecture 7 AIM

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CT002-3-2 AI Methods

Swarm Intelligence, technique

and application-I


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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#

CT002-3.5-2 AI-Methods

$earning outcome#

• To describe an agent in AI

• To explain the swarm techni!es based on animal

beha"ior

• To !nderstand the concept o# colon$% optimi&ationproblems and the bene#its o# swarm intelligence


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WHAT IS ARTIFICIAL INTELLIGENCE?

'(ohn McCarth)% who coined the term de#ines it as "the

science and engineering of making intelligent

machines."

%rtificial Intelligence &%l' is the intelligence o# machines

and the branch o# computer #cience which aims to create it.

The st!d$ and design o# intelligent agents% which is a

s$stem that percei"es its en"ironment and ta*es actions

which maximi&e its chances o# s!ccess.


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What is a Swarm?

• Is the collecti"e beha"ior o# decentrali&ed% sel#-organi&ed s$stems%nat!ral or arti#icial. The concept is emplo$ed in wor* on arti#icial

intelligence.

• A loosel$ str!ct!red collection o# interacting agents

+ Agents,

• Indi"id!als that belong to a gro!p b!t are not necessaril$

identical

• The$ contrib!te to and bene#it #rom the gro!p

• The$ can recogni&e% comm!nicate% and/or interact with each

other • The instincti"e perception o# swarms is a gro!p o# agents in motion +

b!t that does not alwa$s ha"e to be the case.

• A swarm is better !nderstood i# tho!ght o# as agents exhibiting a

collecti"e beha"ior


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Introduction

• onabea! has de#ined swarm intelligence as

“any attempt to design algorithms or distributed problem-solvingdevices inspired by the collective behaviour of social insect colonies

and other animal societies” [1]

• Term swarm in general re#er to an$ restrainedcollection o# interacting agents or indi"id!als

• Two #!ndamental concepts sel#-organi&ation anddi"ision o# labo!r % are necessar$ and s!##icientproperties to obtain swarm intelligent beha"io!r


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E(ample of Swarm

• gro!p #oraging o# social insects• cooperati"e transportation

• di"ision o# labo!r

• nest-b!ilding o# social insects

• collecti"e sorting and cl!stering

)h* SI intere#ting in I+

• distrib!ted s$stem o# interacting a!tonomo!s agents

• goals, per#ormance optimi&ation and rob!stness• sel#-organi&ed control and cooperation decentrali&ed

• di"ision o# labo!r and distrib!ted tas* allocation

• indirect interactions


7/28CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#CT002-3.5-2 AI-Methods

Swarm Intelligence (SI)

• An arti#icial intelligence AI techni!e based on thecollective behavior in decentralized % self-organized

systems.

• 1enerall$ made !p o# agents who interact with each

other and the en"ironment.• o centrali&ed control str!ct!res.

• ased on gro!p beha"ior #o!nd in nat!re.

It does not arise #rom a rationale choice

It does not arise #rom an engineering #inali&ed anal$sis

o one and nothing in the s$stem sa$s, I will do that

beca!se this will lead to a speci#ic beha"ior o# the s$stem

o% intelligence seems to magicall$ 4emerge

“The emergent collectiveintelligence of groups of

simple agents.”

&ona"eau et al, 1...'



Some algorithm# u#ing S)%/

• %nt colon* optimiation &%C2' is a classo# optimi&ation algorithms modeled on the actions o# an ant colon$.

• AC6 methods are !se#!l in problems that need to #ind paths to goals.

Arti#icial 7ants' sim!lation agents locate optimal sol!tions b$ mo"ing

thro!gh a parameter space representing all possible sol!tions.

• at!ral ants la$ down pheromones directing each other to reso!rces

while exploring their en"ironment.

• The sim!lated 7ants7 similarl$ record their positions and the !alit$ o#

their sol!tions% so that in later sim!lation iterations more ants locate

better sol!tions.

• %rtificial "ee colon* algorithm &%C' is a meta-he!ristic algorithm

and sim!lates the #oraging beha"io!r o# hone$ bees. The AC

algorithm has three phases,

emplo$ed bee - onloo*er bee - sco!t bee.



Some algorithm u#ing S)%/

• %C in the emplo$ed bee and the onloo*er bee phases% bees

exploit the so!rces b$ local searches in the neighbo!rhood o# the

sol!tions selected based on deterministic selection in the emplo$ed

bee phase and the probabilistic selection in the onloo*er bee phase.

• The algorithm has a well-balanced exploration and exploitationabilit$.

• %rtificial immune #*#tem# &%IS' concerns the !sage o# abstract

str!ct!re and #!nction o# the imm!ne s$stem to comp!tational

s$stems% and in"estigating the application o# these s$stems towards

sol"ing comp!tational problems .• %IS is a s!b-#ield o# iologicall$ inspired comp!ting% and nat!ral

comp!tation% with interests in Machine 8earning and belonging to

the broader #ield o# Arti#icial Intelligence.



Some algorithm u#ing S)%/

• !article #warm optimiation &!S2' is a global

optimi&ation algorithm #or dealing with problems in which a best

sol!tion can be represented as a point or s!r#ace in an n-

dimensional space.

• 9$potheses are plotted in this space and seeded with aninitial "elocit$% as well as a comm!nication channel between the

particles.

• :articles then mo"e thro!gh the sol!tion space% and are e"al!ated

according to some #itness criterion a#ter each timestep.



Examles !" Swarms in Nat#re$

warms b!ild colonies and wor* in a coordinated manner - $et no single

member o# the swarm is in control.

Termites b!ild giant str!ct!res.

Ants manage to #ind #ood so!rces !ic*l$ and e##icientl$.

;loc*s o# birds coordinate to mo"e witho!t collision.

chools o# #ish #end o## predators and mo"e as one bod$



Swarm Intelligence

warm optimi&ation imitates h!man or insects social

beha"ior.

Indi"id!als interact with one another while learning #rom

their own experience% and grad!all$ mo"e towards the

goal.

It is easil$ implemented and has pro"en both "er$ e##ecti"e

and !ic* when applied to a di"erse set o# optimi&ation

problems.

“Solves optimization problems”



%ees

• Colon* cooperation

• /egulate hie temperature

• Efficienc* ia Specialiation:

dii#ion of la"our in the colon*

• Communication : ood #ource#are e(ploited according to

qualit* and di#tance from the

hie



Termites

• Cone-#haped outer wall#

and entilation duct#

• rood cham"er# in central

hie

• Spiral cooling ent#

• Support pillar#



Self-organiation in a termite #imulation



Ants

• 2rganiing highwa*# to and

from their foraging #ite# "*

leaing pheromone trail#

• orm chain# from their own"odie# to create a "ridge to

pull and hold leaf# together

with #il4

• Dii#ion of la"our "etween

ma5or and minor ant#



Bird Flock

Fish School



)h* do animal# #warm

• To #orage better

• To migrate

• As a de#ense against predators

Termites swarm to b!ild colonies

irds swarm to #ind #ood

ees swarm to reprod!ce

Social Insects have survived for millions of

ears


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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#CT002-3.5-2 AI-Methods

S!cial Insects

• !ro"lem #oling "enefit# include:

+ le(i"le

+ /o"u#t

+ Decentralied

+ Self-2rganied


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S#mmar& !" Insects

The complexit$ and sophistication o#

el#-6rgani&ation is carried o!t with no clear leader


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Swarm Intelligence in +heor*

!n Indepth #ook at $eal !nt Behaviour

Interrupt The Flo%


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The &ath Thickens'

The (e% Shortest &ath


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!dapting to )nvironment *hanges


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Identi#ication o# analogies, in swarm biolog$ and IT

s$stems >nderstanding, comp!ter modeling o# realistic swarm

biolog$

?ngineering, model simpli#ication and t!ning #or IT

applications

6ow can we de#ign SI #*#tem#

+he 3 #tep# proce##e# -


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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#CT002-3.5-2 AI-MethodsC+00-389- &%rtificial Intelligence

Some o"#eration#888

e#t-"uilding in #ocial wa#p#

;roup defen#e in hone* "ee#


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)h* are ant# intere#ting

Ants sol"e complex tas*s b$ simple local means

Ant prod!cti"it$ is better than the s!m o# their single acti"ities

Ants are 'grand masters) in search and exploitation

)hich mechani#m# are important

Cooperation and di"ision o# labo!r Adapti"e tas* allocation


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< = %


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e(t +opic

S)%/ II

• =etails !nderstand on warm concept,

a el# organi&ation

b =i"ision o# labor

c @eprod!ction

d ;oraging

e etc

• =isc!ss AC algorithm

• t!d$ o# timerg$ in

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