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MCA
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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