7/27/2019 KBS INTRO

1/12

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

Intelligence

Dictionary definition

(1) The ability to learn or understand or to deal with new situations

(2) The ability to apply knowledge to manipulate one's environment or to think abstractly as

measured by objective criteria (as tests)

Types of intelligence (Multiple intelligence theory !o"#r$ G#r$ner%

i) eneral intelligence! "

#bilities that allow us to be fle$ible and adaptive thinkers% not necessarily tied to

ac&uired knowledge

ii)

inguistic"verbal intelligence! "

se words and language in various forms * #bility to manipulate language to

e$press oneself poetically

iii) ogical"+athematical intelligence! "

#bility to detect patterns * #pproach problems logically * ,eason deductivelyiv) +usical intelligence! "

,ecogni-e nonverbal sounds! pitch% rhythm% and tonal patterns

v) .patial intelligence! "

Typically thinks in images and pictures * sed in both arts and sciences

vi) /ntrapersonal intelligence! "

#bility to understand oneself% including feelings and motivations * 0an discipline

themselves to accomplish a wide variety of tasks

vii) /nterpersonal intelligence! "

#bility to read peoplediscriminate among other individuals especially their

moods% intentions% motivations3 * #dept at group work% typically assume a

leadership role

viii) 4aturalist intelligence! "

#bility to recogni-e and classify living things like plants% animals

i$) 5odily"6inesthetic intelligence! "

se one7s mental abilities to coordinate one7s own bodily movements

4ote!

nderstanding the various types of intelligence provides theoretical foundations for recogni-ing different

talents and abilities in people

8hat makes life interesting% however% is that we don7t have the same strength in each intelligence area%and we don7t have the same amalgam of intelligences 9ust as we look different from one another and

have different kinds of personalities% we also have different kinds of minds

1

7/27/2019 KBS INTRO

2/12

Defining Artifici#l Intelligence (A I%

There is no agreed definition of the term artificial intelligence :owever% there are various definitions that

have been proposed These are considered below

#/ is a study in which computer systems are made that think like human beings :augeland% 1; 5ellman% 1;? 6night

#/ is the study of computations that make it possible to perceive% reason and act 8inston% 1;;2

#/ is considered to be a study that seeks to e$plain and emulate intelligent behaviour in terms of

computational processes .chalkeoff% 1;;@

#/ is considered to be a branch of computer science that is concerned with the automation of

intelligent behavior uger > .tubblefield% 1;;A

Artificial Intelligence is the development of systems that exhibit the characteristics we associate with

intelligence in human behavior: perception, natural language processing, reasoning, planning andproblem solving, learning and adaptation, etc.

!istory

1;BA +c0ulloch > Citts! 5oolean circuit model of brain

1;=@ Turing7s 0omputing +achinery and /ntelligenceE

1;=@s Farly #/ programs% including .amuel7s checkers (draughts) program

4ewell > .imon7s ogic Theorist% elernter7s eometry Fngine

1;=G Dartmouth meeting! #rtificial /ntelligenceE adopted

1;GGH?B #/ discovers computational comple$ity% 4eural network research almost disappears 1;G;H?; Farly development of knowledge"based systems

1;

7/27/2019 KBS INTRO

3/12

TURING TE&T FOR INTELLIGENCE (ACT !UMANL'%

/nterpersonal link

(telete$t)

,oom I1 ,oom I2 ,oom IA

The human interrogator thinks he*she is communicating with a human

To pass Turing Test the computer must!

Crocess natural language3

,epresent knowledge3

,eason3

earn and adapt to the new situations

Total Turing test included vision > robotics

FEATURE& T!AT C!ARACTERI&TIC& ARTIFICIAL INTELLIGENCE

#% &y)olic *rocessing

#/ emphasi-es manipulation of symbols rather than numbersThe manner in which symbols are processed is non"algorithmic since most human reasoning process do

not necessarily follow a step by step approach (algorithmic approach)

)% !euristics

#re similar to rules of thumb where you need not rethink completely what to do every time a similar

problem is encountered

c% Inferencing

This is a form of reasoning with facts and rules using heuristics or some search strategies

$% *#ttern #tching

# process of describing objects% events or processes in terms of their &ualitative features and logical and

computational relationships

e% +no"le$ge *rocessing

6nowledge consists of facts% concepts% theories% heuristics methods% procedures and relationships

f% +no"le$ge )#ses,

0ollection of knowledge related to a problem or an opportunity used in problem

,easoning occurs based on this knowledge base

The use of a 65 in artificial systems is depicted below

Inputs Coputer

6nowledge

5ase

/nferencing

0apability

A

0omputer:uman/nterrogator

Jutputs (answers

alternatives%solutions etc)/nputs (&uestions%

problems% etc)

:uman 5eing

7/27/2019 KBS INTRO

4/12

Contr#sting Artifici#l #n$ N#tur#l Intelligence

/mportant commercial advantages of #/ are!"

(1) #/ is permanent as long as computer system and prongs remain unchanged

(2) #/ offers ease of duplications and dissemination vs long apprenticeship

(A) #/ can be less e$pensive than natural intelligence

(B) #/ being a computer technician is consistent and thorough nature intelligence may be erratic since

people are erratic% they don7t perform consistently(=) #/ can e$ecute certain tasks much faster than human can

(G) #/ can perform certain tasks better than many or even most people

(?) 4atural /ntelligence has the following advantages

(

7/27/2019 KBS INTRO

5/12

N#tur#l l#ngu#ge un$erst#n$ing" investigates ways of enabling computers to comprehend

instructions given in ordinary Fnglish so that they can understand people more easily

N#tur#l L#ngu#ge Gener#tion" .trives to have computers produces ordinary Fnglish language

so that people can understand them more easily

Jn maturity computers should speak in natural language and understand natural language

$) *ro)le &ol0ing

#imed at building speciali-ed problem solvers eg F.

The challenges faced are!o :ow to formulate problem%

o ooking or searching for solutions and

o ,epresenting knowledge

e) Ro)otics,

",obotics combines sensory systems with mechanical motion to produce machines of varying

intelligences and ability

".ensory systems include vision systems% tactile systems% signal processing systems etc

+ain challenges of #/ research include!

o #rm positioning and

o

ocation positioningsed in industries eg motor vehicles assembly% welding% etc

f% M#chine Le#rning

Kocused in making computers ac&uire knowledge% skills and be adaptive

The challenges are!

o 6nowledge ac&uisition%

o 6nowledge representation%

o earning operators and

o :ow to help human learn

Jn maturity computers are e$pected to learn from e$perience% solve problems and be adaptive

g% Intelligent Agents

&oe Applic#tions Are#s of AI/Cl#ss $icussions

#pplication domain areas include

+ilitary and +edicine

/ndustry and entertainment

Fducation and 5usiness

C,J5F+ .JL/4 TF0:4/MF. /4 #,T/K/0/# /4TF/F40F

Croblems are tackled in #/ using two main broad approaches namely!

i) .earch techni&ue

ii) +odelling natural phenomena (eg evolution and neural networks)

SEAR!I"# AS A $R%&'E( S%')I"# *E!"I+E.ince searching is e$tensively used% we look at searching as a techni&ue of solving problems in more

detail Searching is the process of looking for the solution of a problem through a set of possibilities

(state space)

Search conditions include!

0urrent state "where one is3

oal state H the solution reached3 check whether it has been reached3

0ost of obtaining the solution

*he solution is a path from the current state to the goal state.*rocess of &e#rching

.earching proceeds as follows!

=

7/27/2019 KBS INTRO

6/12

1) 0heck the current state3

2) F$ecute allowable actions to move to the ne$t state3

A) 0heck if the new state is the solution state3 if it is not then the new state becomes the current state and

the process is repeated until a solution is found or the state space is e$hausted

&e#rch pro)le

The search problem consists of finding a solution plan% which is a path from the current state to the goal

state

Representing search problems# search problem is represented using a directed graph The states are represented as nodes while the

allowed steps or actions are represented as arcs

-efining a search problem

# search problem is defined by specifying!

.tate space"set of possibilities

.tart node3

oal condition% and a test to check whether the goal condition is met3

,ules giving how to change states

Example of a search case study

Three blocks #% 5% 0 on a table are considered # block can be grasped when there is no other block on

top of it Jnly one block can be moved at a time

$ossible moves

Cut a block on table3

Cut a block on top of another block3

,emove a block from the top of another and place on top of another block

$roblem

/nitial state (current state)

#

5

Go#l st#te (fin#l st#te%

A

5

0

&t#te sp#ce C &

A # # #

- 50 50 0

0 5

5 5 0 0

# #0 #50 #5 #

5 0

#0 #5

A #- 0

G

7/27/2019 KBS INTRO

7/12

C 5

The state space has 1A elements or nodes

The solution to our problem is any member of the set of all paths from original to goal state such as the

path indicated in bold

#eneral search algorithm

Kunction search (Croblem% MueuingKn)! .olutionNKailure3

Lar nodes!structure3

5egin

8hile

5egin

4ode!O removePfrontPnode(nodes)3

/f oalPtest(problem% .T#TF(node)) succeeds then

.olution!O4ode

Flse

4odes!OMueuingKn(node% JCF,#TJ,(problem))3Fnd3

Fnd8hile

Fnd3

&e#rch &tr#tegies

a) 5lind .earch

b) :euristic .earch

E0#lu#tion of &e#rch &tr#tegies

Kour 0riteria!

0ompleteness" /f a solution e$ists it is guaranteed to be found

Time 0omple$ity" Time taken to find the solution

.pace 0omple$ity"+emory re&uired to perform search

Jptimality"Kinding of the highest"&uality solution when a number e$ist

#% E2h#usti0e 3-lin$ &e#rch &tr#tegies

4o information about the cost or number of steps to reach the goal is used to guide the search

.earching may yield a solution or the state space may be e$hausted without a solution

Two main types of blind search

o Depth"first .earch (DK.)

o 5readth"first .earch (5K.)

o 0.

These strategies differ in the order in which nodes are e$panded

5lind .earch &uickly leads to search spaces that are too large

&readth irst Search /&S0

F$pands all nodes at one level before moving to the ne$t ie the branch (child) nodes are visited first

This is search strategy in which the nodes of the same level are visited first

Kinds the shallowest goal state

ses a lot of memory because the entire search tree must be stored

o .pace comple$ity is a problem

0omplete

Jptimal if cost is directly related to depth

?

7/27/2019 KBS INTRO

8/12

+odifications! niform 0ost .earch

The arrows show the breadth first search progression

+EE1path only containing the root

WHILE QUEUE is non-empty AND goal not reached DO

emo!e the "irst path "rom the QUEUECreate ne# paths $to all children%

e&ect ne# paths #ith loops

Add paths to the 'AC( o" the QUEUE

I) goal reached *HEN s+ccess ELSE "ail+re

Unifor Cost &e#rch

Q F$pands the lowest cost node% rather than the lowest depth node

Q .olution is guaranteed to be the cheapest if the path cost function is non"decreasing

-epth irst Search /-S0

Q F$pands nodes at the deepest level of the search tree

Q .tores only one path from root to leaf

Q .pace comple$ity not a problem

Q Time could be wasted going down the wrong branch of the tree

Q The above is really bad if there is an infinite% or really big search tree

Q 4either complete nor optimal

Q +odifications!

Q Depth imited .earch

Q /terative Deepening .earch

The arrows show the depth first search progression