ai unit 1 complete

8/8/2019 Ai Unit 1 Complete

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MCA

502

Artificial

IntelligenceUnit1


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Whatis

intelligence?

Natural and artificial intelligence

The word intelligence comes from the latin intelligo whichmeans I understand. The basic meaning of intelligence isthe ability to understand or to get it so as to react.

Intelligence is the computational part of the natural ability toachieve desired goals in the world.

The ability to respond quickly, according to the situation

using natural intelligence.The ability to respond quickly, to make out of ambiguous,

contradictory and incomplete information, by recognizingthe relative importance of the different elements of asituation, and to find similarities in dissimilar situations andvice versa comes from the natural intelligence.


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Whatis

Artificial

intelligence?

Theartofcreatingmachinesthatperformfunctionsthat

require

intelligence

when

performed

by

people.

Thebranchofcomputersciencethatisconcernedwith

the

automation

of

intelligent

behavior


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5

Whatis

AI?

Thinking humanly Thinking rationally

Acting humanly Acting rationally


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AITechnique

AItechniqueisamethodthatexploitsknowledgethatshouldbe

representedin

such

away

that:

Knowledgecapturesgeneralization

Itcanbeunderstoodbypeoplewhomustprovideit

Itcanbeeasilymodifiedtocorrecterrors.

Itcanbeusedinvarietyofsituations

IntelligencerequiresKnowledge

Knowledgepossesseslessdesirablepropertiessuchas:

Voluminous Hardtocharacterizeaccurately

Constantlychanging

Differsfromdatathatcanbeused


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Measuringthedegreeofintelligence

Thesetestareappliedtomeasurethedegreeoftheintelligence and

level

of

machine

understanding

achieved.


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Testingthe

intelligence

Testingtheintelligence

Turingtestbyalan turing in1950

Chineseroomexperimentbyjohnsearle


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Chineseroom

experiment


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AIApplication

AI Application area

Mundane tasks

Perception (vision and speech)

Natural language understanding, generations and translation

Commonsense reasoning

Robot controlling

Formal tasks

Games and mathematics

Expert task

Engineering, scientific analysis, medical analysis, financialanalysis


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AIApplication

Plenty of real world task are so ordinary and seem unchallenging; they

cannot attract much consideration by the way they are carried out. Alist of such mundane tasks are given here:

Perception (vision and speech)

Natural language understanding, generations and translation

Commonsense reasoning

Robot controlling

Attempts have been made for computers to see and hear. These have

achieved very limited success. Because useful processing of complexinput data requires understanding, and understanding in turn requireslarge amount of knowledge

It has been difficult to build computes that can generate andunderstand even fragments of a natural language like English.


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AIApplication

Formal tasks often deal with handling large and complex domain

space for problem solving. Without AI intervention it is difficult tosolve such tasks. A few other tasks are:

Games

mathematics

Games can generate enormously sizeable search spaces. Providing a mathematical theorem is an intensive intellectual task. It

requires deductions from hypotheses and involves judgment.

Expert tasks are the tasks that require specialized knowledge to

provide expert conclusions in the specific area. The followingapplications fall under this category. Engineering (design, fault finding, manufacturing, planning, etc.)

Scientific analysis

Medical diagnosis Financial analysis


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DataPyramidANDComputerbasedsystems

DataPyramidANDComputerbasedsystems:ArtificialintelligencesystemsuseAItechniques,throughwhichtheyachieveexpertlevel

competence

in

solving

problems

in

given

areas.

Suchsystems,whichuseoneormoreexpertsknowledgetosolveproblemsinaspecificdomain,

arecalled

knowledge

based

systems.


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DataPyramidANDComputerbasedsystems Traditional information systems work on data and/or information.

Next figures represent the data pyramid showing relationships betweendata, information, knowledge and intelligence.

Figure 1.6 shows the convergence of data to knowledge by applyingactivities like researching, absorbing, acting, interacting, andreflecting.

These activities are shown on the xaxis. While performing theseactivities, a human normally gains understanding and experience, and

may come with innovative ideas. The yaxis presents forms ofconvergence, which are namely raw observation, concepts, rules, and

models and heuristics.

Figure 1.7 shows the data pyramid through management perspectives.


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Convergencefromdatatointelligence


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Datapyramid:managerialperspective


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DataPyramidANDComputerbasedsystems The operational level staff generally work with the structured

environment and uses predefined procedures to carry out the routinetransitions of the business, which are its base operations.

To carry out these operations, the operational staff uses a system like aTransition processing system (TPS). With the structured environment

and a set of predefined procedures, the development and automationof such TPS systems becomes easy.

TPS considers raw observations of the field and processes them to

generate meaningful information. This is the data level of the pyramid. The information generated through these business transitions is

analyzed to form routine and exceptional reports, which are helpful tothe managers and executives when making decisions. The system that

does this is called the Management Information System (MIS). TPS and MIS work on structured environments utilizing data or/or

information.


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DataPyramidANDComputerbasedsystems Management also needs to make decisions considering the costbenefit

ratio of the different solutions available to effectively utilize scarceresources and environmental constraints.

The system used for this is a Decision Support System (DSS).

Unlike TPS, which uses database only and works in structuredenvironments, the DSS normally works on structured to semistructured environments and utilizes the model base and database foroptimum utilization of resources.

Systems like TPS, MIS, and DSS carry out routine businesstransactions, provide detailed analysis of the information generated,and support the decisionmaking processes of the business. However,these systems neither make decisions themselves nor justify them with

proper explanation and reasoning, as they do not possess the requiredknowledge.


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DataPyramidANDComputerbasedsystems Higher level management needs knowledge and wisdom for policy and

strategy making; hence there is a need for knowledge based andwisdom based systems.

By applying morals, principles, and judgments to the decision taken,and after a level of maturity (experience) is gained, information can be

generalized and converted into knowledge.

Many researchers are devoted to developing a truly intelligent system,and computer hardware and software innovations have started takingshape in the last half century. During this time, computer sciencehave traversed the phase of DATA, INFORMATION, andKNOWLEDGE. It is now the 21st centurys challenge to develop a trulyintelligent system. Next figure shows a typical information system in a

tree form. (next slide figure) Next to next we discuss the data, information, knowledge and

intelligence phases.


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CBIStree


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Importantphases

DATA: factual, discrete and static things, and raw observations of the given

area of interest are known as data. Information can be generated bysystematic processing of data. Data are often identified as numeric valueswithin the environment. Data can also be observed as the transactional,physical records of an enterprise's activities, which can be considered as abasic building blocks of any information system.

INFORMATION: information is the processed data. which makesdecision making easier. Processing involves an aggregation of data,calculations of data, corrections on data etc. information usually has somemeaning and purpose that is data within a context can be consideredinformation. Information is actually contextualized, categorized,calculated, corrected and condensed data.

KNOWLEDGE: knowledge is considered a human understanding of asubject matter that has been acquired through proper study and

experience. Knowledge is always concern with learning, thinking andproper understanding.


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Importantphases

WISDOMEANDINTELLIGENCE: knowledgeofconceptsand

models

leads

to

ahigher

level

of

knowledge

called

wisdom.

One

needstoapplymorals,principles,andexpertisetogainandutilizewisdom.

SKILLSVERSUS

KNOWLEDGE:

skillsin

problem

solving

generallyimpliesspeed,efficiency,reducederrors,reducedcognitiveload,robustnessetc.knowledgeontheotherhandallowshumantosolvenewproblemsthroughanalogies,commonsense,analysisandso

on.


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Knowledge based systemsKnowledge based systems: A knowledge based system is one of

the major family members of the AI group. With the availability ofadvanced computing facilities and other resources, attention is nowturning to more demanding tasks that might require intelligence.Society and industry are becoming knowledgeoriented and relying ondifferent experts decision

making abilities to solve problems.

A KBS can act as an expert on demand, anytime and anywhere.

A KBS can save money by leveraging experts, allowing users to function

at a higher level and promoting consistency. It is one productive tool that offers collective knowledge of one or more

experts.

Diff b t t diti l t b d


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Differencebetweentraditional computerbased

informationsystemandknowledgebasedsystem

TraditionalCBIS Knowledge

based

system

1. Givesguaranteedsolutionandconcentratesonefficiency

1.Addspowertothesolutionandconcentrate oneffectivenesswithoutguaranteed

2.Here

data

and/or

information

processingapproach2.

Knowledge

and/or decision

processingapproach

3. ExamplesareTPS,MIS,DSS,etc 3.Examplesareexpertsystems, CASEbasedsystems,etc.

4.Manipulationmethodisnumeric andalgorithmic

4.Manipulationmethodisprimarilysymbolic andnonalgorithmic

5.Thesesystems donotmakemistakes 5. Thesesystemlearnbymistakes

6. Needcomplete

information

and/or

data6.

Partial

and

uncertain

information,

data,orknowledgewilldo

7.Worksforcomplex, integrated,andwideareasinareactivemanner

7. Worksfornarrowdomainsinareactiveandproactivemanner

8.Assists

in

activities

related to

decisionmakingandroutinetransaction.

8.Transfer of

knowledge,

explain

itand

upgradeit.


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Knowledge based systems TheKBSstartedwithexpertsystems,andmanyKBSsolutions

currentlyare

in

use.

Infact,aKBSisacomputerbasedsystemthatusesandgeneratesknowledgefromdata,information,andknowledge.

Thesesystemsarecapableofunderstandingtheinformationbeing

processedand

can

make

adecision

based

on

it,

whereas

the

traditional

computersystemsdonotknoworunderstandthedata/informationtheyprocess.

OBJECTIVESOFKBS:KBSisanexampleoffifthgenerationcomputer

technology.

some

of

its

objectives

are

as

follows. Providesahighintelligencelevel

Assistspeopleindiscoveringanddevelopingunknownfields

Offersavastamountofknowledgeindifferentareas.

Aids

in

management

of

knowledge

stored

in

the

knowledge

base.


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Knowledge based systems Solvessocialproblemsinabetterwaythanthetraditional

computerbased

information

systems.

ComponentsofKBS:theKBSconsistsofaknowledgebaseandasearchprogramcalledaninferenceengine(IE).TheIEisasoftware

programthat

infers

the

knowledge

available

in

the

knowledge

base.

Theknowledgebasecanbeusedasarepositoryofknowledgeinvariousforms.

Expertsknowledgeisacquiredandstoredintheknowledgebase.

AKBS

may

be

either

manually

updated

or

automatically

updated

by

a

machine.Inadditionthereshouldbeanappropriateuserinterface.

Whichmayhavethenaturallanguageprocessingfacility.

thesecomponent

have

been

shown

in

the

next

slide


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Knowledge based systems


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Knowledge based systems


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1.Expertsystems


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Expert

systems


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ExampleES

First expert system DENDRAL for interpreting massspectrogram data to determine molecular structurebyBuchanan, Feigenbaum, and Lederberg (1969).

Early expert systems developed for other tasks:

MYCIN: diagnosis of bacterial infection (1975)

PROSPECTOR: Found molybendum deposit

based on geological data (1979) R1: Configure computers for DEC (1982)


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2.Linked

system

Hypermedia systems, such as hypertext, hyperaudio, and hypervidio,

are considered linked knowledge based systems. Such linked systemscontain nonsequentially linked text, audio, and video chunksgenerated during processing.

These components are linked in such a way that they generate meaning

and exhibit intelligence.


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3.CASEBased

systems

Intelligence systems for computeraided software engineering (CASE)

are another type of KBS. These systems guide the development of information/intelligent

systems for better quality and effectiveness.

These systems also help in risk management and support projectmanagement activities during development.


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4.Databasemanagement

systems

in

conjunction

withanIntelligentuserinterface

Recent database management system offer a userfriendly interface to

the data being stored. With the help of a query languages, informationcan be effectively extracted for users.

However, such an interface is limited in that it cannot handle partialinformation in a natural language and can not make or justify decisions

for itself. An intelligent user interface can enhance the use of the content

available in the traditional format.

Interacting with users in their own language might increase theefficiency of decision making.


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5.Intelligenttutoringsystem Training, educating, and motivating users are important aspects of a

tutoring system. Knowledge based systems are used to train and guide students,

trainers, and practitioners in specific areas and at different levels.

Such systems are used to identify the users level and other constraintsto provide training in different technical and nontechnical areas.

One wellknown branch of intelligent tutoring system is dialogbasedtutoring systems.


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DifficultieswiththeKBS Completeness of knowledge base: most of the system have a great deal

of limited knowledge about a focused subset of a problem and verylittle knowledge about anything else.

Characteristics of knowledge: knowledge is constantly changing. Thismakes the development of a knowledgebased system more difficult.

Large size of knowledge base: to solve even a simple problem, a largeamount of knowledge is required. The voluminous knowledge basemakes the management task more difficult.

Acquisition of knowledge: Acquiring knowledge from one or moreexperts has always been difficult, tedious, and costly process. Theknowledge engineer, who is responsible for the acquisition process,should identify and represent knowledge. It is knowledge engineers

knowledge that is reflected in the system, not the experts knowledge.


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DifficultieswiththeKBS

Slow learning and execution: once implemented the KBS model isoften slow and unable to access or manage large volumes ofinformation; once implemented it can be difficult to maintain.


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KnowledgebasedsystemArchitecture

Sourceoftheknowledge:aswesawinthedatapyramid

knowledgeis

difficult

to

acquire

directly

it

needs

to

be

processed

from

rawobservationsandinformationfromthedomain.

Thebasicsourceoftheknowledgeisthehumanmind.

Anotheroftenaccessedresourceforknowledgeisdataandinformationfrom

the

environment.

Experienceofworkinginagivendomain,surveyresults,mediareports,casestudiesandexperts arethemeansthroughwhichknowledgecanbeacquired.

Note:no

formal

method

exists

for

knowledge

acquisition,

such

as

the

fact

findingmethodssuggestedinthedisciplinessoftwareengineeringorsystemsanalysisanddesigndisciplines.


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KnowledgebasedsystemArchitecture


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Knowledge in any field is usually of two types:

Public knowledge: Public knowledge includes the published definitions,facts, and theories, available in textbooks, journals, research papers, and soon. But public knowledge is not the only source of human expertise.

Private knowledge: human experts generally possess private knowledge.

Private knowledge consists largely of rules of thumb, also called heuristics.Heuristics enable human experts to make educated guesses whennecessary, recognize promising approaches to problem solving, and dealeffectively with faulty or incomplete data.


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Types

of

knowledge Commonsense and informed commonsense knowledge:

Heuristic knowledge

Domain knowledge

Metaknowledge

Classifying knowledge according to its Use

Conditional knowledge

Utility knowledge

Action knowledge

Goal knowledge

Classifying knowledge according to its nature: another way ofclassifying knowledge is to determine whether it is tacit or explicit.Tacit knowledge is usually embedded in the human mind throughexperience. Explicit knowledge is that which is comparatively easy to

extract and codify into various sources, such as books, media, reports,and so on.


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Desirablecharacteristicsofknowledge

Naturalness:Easeofrepresentingknowledgeinitsnativeform Transparency:

Ease

of

identifying

stored

knowledge

Adequacyandcompleteness:Abilityofknowledgetocontainallcomponentsrequiredtosolvetheproblem.

Modularity:ease

of

storing

knowledge

components

in

parts

to

form

alowerlevelreusablecomponentlibrary.Whichleadstoincreasedcost

effectivenessandstructurednessbyprovidinghighreusability.

Usefulness: Extenttowhichtheknowledgeisusefultosolveaproblem

inthe

domain.

Explicitness: Easeofrepresentingtheknowledgedirectly.

Easeofoperation,easytoaccessandefficient:easeofobtaining,

applying

the

knowledge

to

problem

solving,

and

analyzing

the

results.


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Componentsofknowledge Knowledgeconsistsofproceduralanddeclarativecomponents.

Knowledgeis

adescriptive

representation

of

knowledge

consisting

of

factualstatementsandinformation.Thesearerulesandfacts.

Declarativeknowledgeiseasytoacquireanddocumentintheknowledgebase.

Proceduralknowledgeresultsfromtheintellectualskillstodosomething.Theseskillsusedtomakedecisionsaredifficulttoexplainformostsituations.Thatiswhyitiscomparativelydifficulttowork

with

such

knowledge.

Procedural

knowledge

generally

encompasses

a

sequenceofactions,alongwiththeexpectedresult.Commonsenseknowledgeandheuristicknowledgeareexamplesofproceduralknowledge.


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Componentsof

knowledge

Facts:factsrepresentsetsofrawobservation,alphabets,symbols,orstatements.

Examples

of

facts

are

as

follows:

Theearthmovesaroundthesun

Everycarhasabattery

Rules:

H i ti


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Heuristics


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1.Knowledgebase:


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Knowledgebase: The knowledge base is the key component of a knowledge based

system. The quality and usefulness of the system is directly related tothe knowledge representation in it. The knowledge base contains alltypes of knowledge in a given form.

It is obvious that the knowledge base must contain the domain

knowledge within which the system is intended to solve the problem.Metaknowledge should also be stored. The figure in the previous slideshows the components of the knowledge base.


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2.Inferenceengine Aninferenceengineisasoftwareprogramthatreferstheexisting

knowledge,manipulates

the

knowledge

according

to

need,

and

makes

decisionsaboutactionstobetaken.

Itgenerallyutilizespatternmatchingandsearchtechniquesforconclusions.Throughtheseprocedures,theinferenceengineexamines

existingfacts

and

rules

and

adds

new

facts

when

possible.

Inotherwordsaninferenceenginenotonlyreferstheknowledgeavailablewithintheknowledgebase,butalsoinfersnewknowledgeasneeded.


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Inferenceengine


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Inferenceengine

In simple rulebased systems, there are two kinds of inferences:

forward chaining, backward chaining. Previous figure shows the typicalinference cycle.

Forward chaining: environmental inputs and data are stored in workingmemory. The input of the working memory triggers rules for which

conditions match the new data and constraints. These rules thenperform their actions. The actions may add new data to memory, thustriggering more rules, and so on. This is also called data directedinference, because an inference is triggered by the arrival of new data

in working memory. An inference engine using forward chainingsearches the inference rules until it finds one where an IF clause isknown to be true. When found, it can conclude, or infer the THENclause, which results in the addition of new information to its dataset.


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Forwardchaining


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Backwardchaining


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Conflictresolutionstrategiesforrulebased

KBS The most common and simple strategy to resolve the conflict is to

select the first rule from the conflict set. Here the order in which therules are stored in the conflict set is very important.

One may consider a heuristic approach by managing a simple pointerreferencing how frequently the rule is fired to set the priority.

Another approach is to select the rule with more details or constraints,or that was recently updated.

Selecting the rule randomly is also another possible alternative toresolve the conflict.


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3.Self

learning

Self learning is a scientific task that enable the knowledgebased

system to learn automatically from the inference process, casesexecuted, and environment.


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4.Reasoning The capability and quality of a knowledge based system or human

expert depend upon the ability to reason and explanation provided byexperts. When knowledge based system takes decision it needs to

justify it.


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5.Explanation Presenting a chain of reasoning from the strategic knowledge available

in the knowledge based does not let a human user easily understandthat reasoning. From such knowledge the rules used by the knowledgebased system are compiled, and these knowledge is used to providemore abstract explanations of the systems reasoning.


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ApplicationsofKnowledgebasedsystem Advisorysystems

Healthcare

and

medical

diagnosis

systems

Tutoringsystems

Controlandmonitoring

Prediction Planning

Searchinglargerdatabasesanddatawarehouses

Knowledgebasedgridandsemanticweb


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Knowledgebasedshell A knowledge based shell is a suit of software that allows construction of

a knowledge base and interaction with it using an inference engine. A knowledge based shell provides a fully developed knowledge based

system with an empty knowledge base.

Utilities like inference, explanation, reasoning, and learning are

available in ready made fashion.

That is why such a shell is the most suitable tool for the experts whocan not develop a knowledge based system themselves.

If necessary a sensory interface and knowledge update facility isprovided.


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Knowledgebasedshell Various knowledge based system development tools are available.

They differ in the level of flexibility they provide in the system and inthe knowledge representation, reasoning and other intelligenttechniques they support.

The shell comprises the inference and explanation facilities of the

knowledge based system, without the domain specific knowledge.

This is beneficial to nonprogrammers, who can include their ownknowledge on a problem of a similar structure but reuse the reasoningmechanisms.

A different shell is required for each type of problem, but one shell canbe used for many different domains. Thus selecting a shell with the wrong reasoning strategy for the problem will create more

difficulties than it solves.


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Advantagesofknowledgebasedsystem Permanentdocumentationofknowledge

Cheapersolution

and

easy

availability

of

knowledge

Dualadvantageofeffectivenessandefficiency

Consistencyandreliability

Justification

for

better

understanding Selflearningandeaseofupdate


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Limitationsofknowledgebasedsystem Partialselflearning

Creativityand

innovation

Weaksupportofmethodsandheuristic

Developmentmethodology

Knowledge

acquisition Structuredknowledgerepresentationandontologymapping

Developmentoftestingandcertifyingstrategiesandstandardsforknowledgebasedsystems.


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Productionsystem Sincesearchformsthecoreofmanyintelligentprocesses.Itisusefulto

structureAI

programs

in

away

that

facilitates

describing

the

search

process.Productionsystemsprovidesuchstructure.Aproductionsystemconsistsof:

Asetofrules,eachconsistingofaleftside(apattern)thatdeterminesthe

applicabilityof

the

rule

and

aright

side

that

describes

the

operation

to

be

performediftheruleisapplied.

Oneormoreknowledge/databasesthatcontainwhateverinformationisappropriatefortheparticulartask.Somepartofthedatabasemaybe

permanent,

while

other

parts

of

it

may

pertain

only

to

the

solution

of

the

currentproblem.Theinformationinthesedatabasemaybestructuredinanyappropriateway.

Acontrolstrategythatspecifytheorderinwhichtheruleswillbecomparedtothedatabaseandawayofresolvingtheconflictsthatarisewhenseveralrulesmatchatones.

Aruleapplier.


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Productionsystem Controlstrategies:sofarwehavecompletelyignoredthequestionof

howto

decide

which

rule

to

apply

next

during

the

process

of

searching

forasolutiontoaproblem.Thisquestionarisessinceoftenmorethanonerulewillhaveitsleftsidematchthecurrentstate.

Thefirstrequirementofagoodcontrolstrategyisthatitshouldcause

motion. Thesecondrequirementofagoodcontrolstrategyisthatitshouldbe

systematic.


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Algorithm:breathfirstsearch

1.createavariablecalledNODELISTandsetittotheinitialstate

2.Unitilagoal

state

is

found

or

NODE

LIST

is

empty

do:

(a)removethefirstelementfromNODELISTandcallitE.ifNODELISTwasempty,quite

(b)for

each

way

that

each

rule

can

match

the

state

described

in

E

do:i.Applytheruletogenerateanewstate.

ii. ifthenewstateisagoalstate,quiteandreturnthisstate.

iii. otherwise,addthenewstatetotheendofNODELIST.


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Algorithm:depthfirstsearch1. Iftheinitialstateisagoalstate,quiteandreturnsuccess.

2. Otherwise,do

the

following

until

success

or

failure

is

signaled:

(a) Generateasuccessor,E,oftheinitialstate.Iftherearenomoresuccessors,signalfailure.

(b) CalldepthfirstsearchwithEastheinitialsate.

(c) Ifsuccess

is

returned,

signal

success,

otherwise

continue

in

this

loop.


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DFS

BFS


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Generateand

test

1. Generateapossiblesolution

2. Testto

see

if

this

is

actually

asolution

by

comparing

the

chosen

point

3. Ifasolutionhasbeenfoundquite,otherwisereturntostep1.

Exhaustive generate

and

test Heuristic generateandtest


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SimpleHillclimbing Hillclimbingisjustthevariantofgenerateandtest.Butituses

feedback

from

the

search

procedure.1. Evaluatetheinitialstate.Ifitisagoalstate,thenreturnitandquit.

Otherwisecontinuewiththeinitialstateasacurrentstate.

2. Loopuntilasolutionisfoundoruntiltherearenonewoperatorleft

tobe

applied

in

the

current

state.

(a) Selectanoperatorthathasnotyetbeenappliedtothecurrentstateandapplyittoproduceanewstate.

(b) Evaluatethenewstate(a) If

it

is

agoal

state,

then

return

it

and

quit.

(b) Ifitisnotagoalstate,butitisabetterthanthecurrentstate,thenmakeitthecurrentstate.

(c) Ifitisnotbetterthanthecurrentstate,thencontinue intheloop


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SteepestAscent

Hill

climbing

Ausefulvariationonsimplehillclimbingconsidersallthemovesfrom

the

current

state

and

selects

the

best

one

as

the

next

state.

Thismethodiscalledsteepest ascenthillclimbingorgradientsearch.

Noticethatthiscontrastswiththebasicmethodinwhichthefirststatethatisbetterthanthecurrentstateisselected.Thealgorithmworksas

follows.


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Algorithm:SteepestAscenthillClimbing

1. Evaluatetheinitialstate.Ifitisagoalstate,thenreturnitandquite.Otherwisecontinuewiththeinitialstateasthecurrentstate.

2. Loopuntilasolutionisfoundoruntilacompleteiterationproduces

nochange

to

the

current

state:

(a) LetSUCCbeastatesuchthatanypossiblesuccessorofthecurrentstatewillbebetterthanSUCC.

(b) Foreachoperatorthatappliestothecurrentstatedo:i. Apply

the

operator

and

generate

anew

state.

ii. Evaluatethenewstate.Ifitisgoalstate,thenreturnitandquite.Ifnot,compare,ittoSUCC.Ifitisbetter,thensetSUCCtothisstate.Ifitisnotbetter,leaveSUCCalone.

(c) If

the

SUCC

is

better

than

current

state,

then

set

current

state

to

SUCC.


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Problemwith

hill

climbing

Localmaximum:isastatethatisbetterthanallits

neighborsbut

is

not

better

than

some

other

states

fartheraway.Inthiscasetheyarecalledfoothills.

Plateau:isaflatareaofthesearchspaceinwhicha

wholeset

of

neighboring

states

have

the

same

value.

Solutionforlocalmaximumisbacktracking andtrygoinginadifferentdirection.

Solutionforplateauismakeabigjumpinsomedirection totrytogettoanewsectionofthesearch

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