artificialintelligence-090417043101-phpapp02

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Artificial Intelligence

A.I. can be define as the artificial brain having

capability of thinking and understanding.

A.I. is branch of computer science concerned with

the study and creation of computer system that exhibits

some form of intelligence.


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Knowledge-based systems


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Introduction, or what is knowledge?

Knowledge Knowledge can be defined as the body of facts and

principles accumulated by human kind or the act ,fact or

state of knowing

is a theoretical or practical understanding of a subject The sum of what is currently known, and apparently

knowledge is power.


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In biological organisms, knowledge is likely stored as complex

structure of interconnected Neurons.

In computers, knowledge is stored as symbolic structure but in

form of collections of magnetic spots & voltage states


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Knowledge SourcesDocumented (books, manuals, etc.)

Undocumented (in people's minds)

From people, from machines

Knowledge Acquisition from Databases

Knowledge Acquisition Via the Internet


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Knowledge Levels

Shallow knowledge (surface) Deep knowledge

Can implement a computerized representation that is

deeper than shallow knowledge

Special knowledge representation methods (semanticnetworks and frames) to allow the implementation of

deeper-level reasoning (abstraction and analogy):

important expert activity

Represent objects and processes of the domain of

expertise at this level

Relationships among objects are important


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Scope of Knowledge

Knowledge acquisition is the extraction of knowledge from

sources of expertise and its transfer to the knowledge base and

sometimes to the inference engine

Knowledge is a collection of specialized facts, procedures and

judgment rules


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Domain Expert

Those who possess knowledge are called experts.

Anyone can be considered a domain expert if he or she hasdeep knowledge (of both facts and rules) and strong practicalexperience in a particular domain. The area of the domain maybe limited. In general, an expert is a skilful person who can do

things other people cannot. knowledgeable and skilled person capable of solving problems

in a specific area or domain.

Has the greatest expertise in a given domain.

This expertise is to be captured in the expert system.

Therefore, the expert must be able to communicate his orher knowledge, be willing to participate in the expert systemdevelopment and commit a substantial amount of time to theproject.

Most important player in the expert system developmentteam.


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Major Categories of Knowledge

Declarative Knowledge

Procedural Knowledge

Meta knowledge


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Declarative Knowledge

Descriptive Representation of Knowledge

Expressed in a factual statement

Shallow

Important in the initial stage of knowledge acquisition


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Procedural Knowledge

Considers the manner in which things work under different

sets of circumstances

Includes step-by-step sequences and how-to types of

instructions

May also include explanations

Involves automatic response to stimuli

May tell how to use declarative knowledge and how to

make inferences


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Descriptive knowledge relates to a specific object.

Includes information about the meaning, roles,

environment, resources, activities, associations andoutcomes of the object

Procedural knowledge relates to the procedures employed

in the problem-solving process


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Meta knowledge

Knowledge about Knowledge

Meta knowledge can be simply defined as knowledge aboutknowledge.

Meta knowledge is knowledge about the use and control ofdomain knowledge in an expert system.

In ES, Meta knowledge refers to knowledge about the operation ofknowledge-based systems

Its reasoning capabilities


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Whats in the knowledge base?

Facts about the specifics of the world

Northwestern is a private university

The first thing I did at the party was talk to John.

Rules that describe ways to infer new facts from existing facts

All triangles have three sides

All elephants are grey

Facts and rules are stated in a formal languageGenerally some form of logic.


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A major turning point occurred in the field of AI with realization

that in knowledge lies the power.

This realization led to the development of a new class of

system: i.e. knowledgebased system.

knowledgebased system get their power from the expert

knowledge that has been coded into facts , rules & procedure.


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Components of KBS

The knowledge is stored in a knowledge base separated from

the control & inferencing component . This makes it possible

to add new knowledge or refine existing knowledge without

recompiling the control and inferencing programs.

Input output

unit

Inference control

unitKnowledge

base


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Structure and characteristics

AI programs:intelligent problem solving tools

KBSsAI programs with special programstructure separated knowledge base

ESs

KBSs applied in a specific narrow field

AI programs


Expert systems


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The Knowledge Hierarchy

meta-

knowledge

knowledge

informationdata

noise

large volume, low

value, usually no

meaning/ context

lower volume, higher

value, with context and

associated meanings

understanding of a

domain, can be applied to

solve problems

knowledge on knowledge

(e.g how/when to apply)

may contain

irrelevant items

which obscure data

management

information

systems

knowledge-

basedsystems

databases,transaction

systems


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Different type of knowledge base system

There are different type of knowledge base system as1. knowledge engineer

KE involves knowledge of applications of engineering field related to thatsystem.

2 Knowledge representation

The KR means the key topics & concept involved in the system berepresented using a data base system.

3 knowledge use

These are the utilization of knowledge for problem solving tech. thisinvolves analogical reasoning decision related methods and system etc.

4 Knowledge Acquisition

The knowledge has to be acquired by way of learning or by studying thesystem. The transfer of knowledge or exchange of knowledge results intoknowledge acquisition

`


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What is Knowledge Engineering?

the process of building an ES

the effort in developing a large quantity of effective knowledge(i.e. the KB)

the acquisition of knowledge from a human expert or othersource (by a knowledge engineering) and its coding in the ES

KE is important, because:

performance of an ES is largely determined by the quantity &quality of knowledge in its KB


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knowledge Engineering The process of building knowledge-based systems is called

knowledge engineering (KE). It has a great deal in common with

software engineering, and is related to many computer science

domains such as artificial intelligence, databases, data mining,

expert systems, decision support systems and geographicinformation systems. Knowledge engineering is also related to

mathematical logic and cognitive science as the knowledge is

produced by cognitive systems (mainly humans) and is

structured by our understanding of how human reasoning or

logic works.


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Knowledge Engineering

Art of bringing the principles and tools of AI research tobear on difficult applications problems requiring experts'

knowledge for their solutions

Technical issues of acquiring, representing and using

knowledge appropriately to construct and explain lines-of-reasoning

Art of building complex computer programs that represent

and reason with knowledge of the world(Feigenbaum and McCorduck [1983])


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The knowledge engineer

someone who is capable of designing, building and testing

an expert system.

interviews the domain expert to find out how a particular

problem is solved.

establishes what reasoning methods the expert uses tohandle facts and rules and decides how to represent them

in the expert system.

chooses some development software or an expert system

shell, or looks at programming languages for encoding the

knowledge.

responsible for testing, revising and integrating the expert

system into the workplace.


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Knowledge Engineering

Process of acquiring knowledge from experts and building

knowledge base

Narrow perspective

Knowledge acquisition, representation, validation,

inference, maintenance

Broad perspective

Process of developing and maintaining intelligent system


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Views of knowledge engineering

There are two main views to knowledge engineering:

Transfer View This is the traditional view. In this view,the assumption is to apply conventional knowledgeengineering techniques to transfer human knowledge into

artificial intelligence systems.

Modeling View This is the alternative view. In this view,the knowledge engineer attempts to model the knowledgeand problem solving techniques of the domain expert intothe artificial intelligence system.


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Knowledge Engineering Process Activities

Knowledge Acquisition

Knowledge Validation

Knowledge Representation

Inferencing

Explanation and Justification


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Knowledge Engineering Process

Knowledge

validation

(test cases)

Knowledge

Representation

Knowledge

AcquisitionEncoding

Inferencing

Sources of knowledge

(experts, others)

Explanation

justification

Knowledge

base


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Knowledge Engineering in a Nutshell

human

expert

knowledge

engineer

knowledge base

(in ES)

explicit

knowledge

dialog

knowledge

refinement


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Phases of KE

Various phases of KE specific for the development of a knowledge-

based system:

* Assessment of the problem

* Acquisition and structuring of related information,knowledge and specific preferences

* Development of a knowledge-based system shell/structure

* Implementation of the structured knowledge into

knowledge-bases

* Testing and validation of the inserted knowledge* Integration and maintenance of the system

* Revision and evaluation of the system."


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Knowledge Engineering Principles

Knowledge engineers acknowledge that there are different types of knowledge,and that the right approach and technique should be used for the knowledgerequired.

Knowledge engineers acknowledge that there are different types of expertsand expertise, such that methods should be chosen appropriately.

Knowledge engineers recognize that there are different ways of representingknowledge, which can aid the acquisition, validation and re-use of knowledge.

Knowledge engineers recognize that there are different ways of usingknowledge, so that the acquisition process can be guided by the project aims.

Knowledge engineers use structured methods to increase the efficiency of theacquisition process


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The main players in the development team

Expert System

End-user

Knowledge Engineer ProgrammerDomain Expert

Project Manager

Expert System

Development Team


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Intelligent SystemA System can be constructed as a intelligence system if it has fourmajor techniques of knowledge representation.

1.Logic

The logic is a formal procedure because of which implications arecreated from the set of known facts.

2.Production Systems

The production systems studies the new facts and the knownfacts and finds the desired conclusion.

3.Semantic networks

It is a network of symbols that describe relationship betweenelements of knowledge

4.Frames

These are the data structures which consists of expectations for agiven situation.


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Although knowledge representation is oneof the central and in some ways most

familiar concepts in AI, the most

fundamental question about itWhat is it?

has rarely been answered directly.


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What is a knowledge representation?

A knowledge representation (KR) is most fundamentally a surrogate, a substitute forthe thing itself, used to enable an entity to determine consequences by thinking rather

than acting, i.e., by reasoning about the world rather than taking action in it.

It is a set of ontological commitments, i.e., an answer to the question: In what termsshould I think about the world?

It is a fragmentary theory of intelligent reasoning, expressed in terms of threecomponents: (i) the representation's fundamental conception of intelligent reasoning;

(ii) the set of inferences the representation sanctions; and (iii) the set of inferences itrecommends.

It is a medium for pragmatically efficient computation, i.e., the computationalenvironment in which thinking is accomplished. One contribution to this pragmaticefficiency is supplied by the guidance a representation provides for organizinginformation so as to facilitate making the recommended inferences.

It is a medium of human expression, i.e., a language in which we say things about theworld.


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Elements of a Representation

Represented world: about what?

Representing world: using what?

Representing rules: how to map?

Process that uses the representation: conventions and systems

that use the representations resulting from above.

Analog versus Symbolic


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Understanding the roles and acknowledging their diversity has several usefulconsequences. First, each role requires something slightly different from arepresentation; each accordingly leads to an interesting and different set ofproperties we want a representation to have.

Second, we believe the roles provide a framework useful for characterizing awide variety of representations. We suggest that the fundamental "mindset" ofa representation can be captured by understanding how it views each of theroles, and that doing so reveals essential similarities and differences.

Third, we believe that some previous disagreements about representation areusefully disentangled when all five roles are given appropriate consideration.We demonstrate this by revisiting and dissecting the early arguments

concerning frames and logic.

Finally, we believe that viewing representations in this way has consequencesfor both research and practice. For research, this view provides one directanswer to a question of fundamental significance in the field. It also suggestsadopting a broad perspective on what's important about a representation, andit makes the case that one significant part of the representation endeavor--capturing and representing the richness of the natural world--is receiving

insufficient attention.


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Terminology

Two points of terminology will assist in our presentation. First,we use the term inference in a generic sense, to mean anyway to get new expressions from old. We are only rarelytalking about sound logical inference and when doing so refer

to that explicitly.

Second, to give them a single collective name, we refer to thefamiliar set of basic representation tools like logic, rules,frames, semantic nets, etc., as knowledge representationtechnologies.


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We have argued that a knowledge representation plays five distinct roles,each important to the nature of representation and its basic tasks. Thoseroles create multiple, sometimes competing demands, requiring selectiveand intelligent tradeoff among the desired characteristics. Those five rolesalso aid in characterizing clearly the spirit of representations andrepresentation technologies that have been developed.

This view has consequences for both research and practice in the field. Onthe research front it argues for a conception of representation broader than

the one often used, urging that all of the five aspects are essentialrepresentation issues. It argues that the ontological commitment arepresentation supplies is one of its most significant contributions; hencethe commitment should be both substantial and carefully chosen. It alsosuggests that the fundamental task of representation is describing thenatural world and claims that the field would advance furthest by taking this

as its central preoccupation.


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Different levels of knowledge

representation

Mental Imag e

Written Text

Magnet ic Spots

Binary Numbers

Character String s


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How Knowledge Representation Works

Intelligence requires knowledge

Computational models of intelligence require models ofknowledge

Use formalisms to write down knowledge

Expressive enough to capture human knowledge

Precise enough to be understood by machines

Separate knowledge from computational mechanisms thatprocess it

Important part of cognitive model is what the organismknows.


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How knowledge representations are

used in cognitive models

Contents of KB is

part of cognitive

model

Some models

hypothesizemultiple knowledge

bases.

Knowledge

Base

Inference

Mechanism(s)Learning

Mechanism(s)

Examples,Statements

Quest ions,requests

Answers,analyses


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Knowledge acquisition

Knowledge acquisition includes the elicitation,

collection, analysis, modelling and validation ofknowledge forknowledge engineering and

knowledge management projects
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Issues in Knowledge Acquisition

. Some of the most important issues in knowledge acquisition are asfollows:

Most knowledge is in the heads of experts

Experts have vast amounts of knowledge

Experts have a lot of tacit knowledge

They don't know all that they know and use

Tacit knowledge is hard (impossible) to describe

Experts are very busy and valuable people

Each expert doesn't know everything

Knowledge has a "shelf life"


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Requirements for KA Techniques

Because of these issues, techniques are requiredwhich:

Take experts off the job for short time periods

Allow non-experts to understand the knowledge

Focus on the essential knowledge

Can capture tacit knowledge

Allow knowledge to be collated from different

expertsAllow knowledge to be validated and maintained


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KA Techniques Many techniques have been developed to help elicit knowledge from an expert.

These are referred to as knowledge elicitation or knowledge acquisition (KA)techniques. The term "KA techniques" is commonly used.The following list

gives a brief introduction to the types of techniques used for acquiring,

analysing and modelling knowledge:

Protocol-generation techniques include various types of interviews

(unstructured, semi-structured and structured), reporting techniques (such asself-report and shadowing) and observational techniques

Protocol analysis techniques are used with transcripts of interviews or other

text-based information to identify various types of knowledge, such as goals,

decisions, relationships and attributes. This acts as a bridge between the use

of protocol-based techniques and knowledge modelling techniques.

Hierarchy-generation techniques, such as laddering, are used to build

taxonomies or other hierarchical structures such as goal trees and decision

networks.

KA Techniques
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ec ques

Matrix-based techniques involve the construction of grids indicating such thingsas problems encountered against possible solutions. Important types include theuse of frames for representing the properties of concepts and the repertory gridtechnique used to elicit, rate, analyse and categorise the properties of concepts.

Sorting techniques are used for capturing the way people compare and orderconcepts, and can lead to the revelation of knowledge about classes, propertiesand priorities.

Limited-information and constrained-processing tasks are techniques that eitherlimit the time and/or information available to the expert when performing tasks.

For instance, the twenty-questions technique provides an efficient way ofaccessing the key information in a domain in a prioritised order.

Diagram-based techniques include the generation and use of concept maps, statetransition networks, event diagrams and process maps. The use of these isparticularly important in capturing the "what, how, when, who and why" of tasksand events.
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Knowledge Acquisition Methods:

An Overview

Manual

Semiautomatic

Automatic (Computer Aided)

Manual Methods Structured


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Manual Methods Structured

Around

Interviews

Process

Interviewing

Tracking the Reasoning Process

Observing

Manual methods: slow, expensive and sometimes

inaccurate


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Manual Methods of knowledge Acquisition

Knowledge

base

Documented

knowledge

Experts

CodingKnowledge

engineer


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Semiautomatic Methods

Support Experts Directly

Help Knowledge Engineers


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Expert-Driven

Knowledge Acquisition

Knowledge

base

Knowledge

engineer

ExpertCodingComputer-aided

(interactive)

interviewing


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Automatic Methods

Experts and/or the knowledge engineers roles are

minimized (or eliminated)

Induction Method.


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Induction-Driven Knowledge Acquisition

Knowledge

base

Case histories

and examples

Induction

system


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Knowledge Acquisition Difficulties

Problems in Transferring Knowledge

Expressing Knowledge

Transfer to a Machine

Number of Participants

Structuring Knowledge

Other Reasons


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Experts may lack time or not cooperate

Testing and refining knowledge is complicated

Poorly defined methods for knowledge elicitation

System builders may collect knowledge from one source, but the

relevantknowledge may be scattered across several sources

May collect documented knowledge rather than use experts

The knowledge collected may be incomplete

Difficult to recognize specific knowledge when mixed with irrelevant

data

Other Reasons


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Experts may change their behavior when observed and/orinterviewed

Problematic interpersonal communication between theknowledge engineer and the expert

Critical

The ability and personality of the knowledge engineer

Must develop a positive relationship with the expert

The knowledge engineer must create the right impression

Computer-aided knowledge acquisition tools

Extensive integration of the acquisition efforts


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Advantages of KBSs and ESs

make up for shortage of experts, spread expert knowledge on availableprice

field of interest changes are well-tracked

increase expert ability and efficiency

preserve know-how

can be developed systems unrealizabled with tradicional technology(Buck Rogers)

self-consistents in advising, equable in performance are availablepermanently

able to work even with partial, non-complete data

able to give expanation


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Disadvantages of KBSs and ESs

their knowledge is from a narrow field, dont know the limits

the answers are not always correct (advices have to beanalysed!)

dont have common sence (greatest restriction) all of theself-evident checking have to be defined

(many exceptions increase the size of KB and the runningtime)

Role V: A KR is a Medium of Human Expression


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Finally, knowledge representations are also the means bywhich we express things about the world, the medium ofexpression and communication in which we tell the machine(and perhaps one another) about the world. This role forrepresentations is inevitable so long as we need to tell themachine (or other people) about the world, and so long as wedo so by creating and communicating representations. (5) Thefifth role for knowledge representations is thus as a medium ofexpression and communication for use by us.
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Role IV: A KR is a Medium forEfficient Computation

From a purely mechanistic view, reasoning inmachines (and somewhat more debatably, in people)

is a computational process. Simply put, to use arepresentation we must compute with it. As a result,questions about computational efficiency are inevitablycentral to the notion of representation.

Role III: A KR is a Fragmentary Theory Of Intelligent Reasoning


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The third role for a representation is as a fragmentary theory ofintelligent reasoning. This role comes about because the initialconception of a representation is typically motivated by someinsight indicating how people reason intelligently, or by somebelief about what it means to reason intelligently at all.

The theory is fragmentary in two distinct senses: (i) therepresentation typically incorporates only part of the insight or

belief that motivated it, and (ii) that insight or belief is in turn onlya part of the complex and multi-faceted phenomenon ofintelligent reasoning.

A representation's theory of intelligent reasoning is often implicit,but can be made more evident by examining its threecomponents: (i) the representation's fundamental conception ofintelligent inference; (ii) the set of inferences the representationsanctions; and (iii) the set of inferences it recommends.


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Role II: A KR is a Set of Ontological Commitments

If, as we have argued, all representations are imperfect

approximations to reality, each approximation attending tosome things and ignoring others, then in selecting any

representation we are in the very same act unavoidably making

a set of decisions about how and what to see in the world. That

is, selecting a representation means making a set of

ontological commitments. (2) The commitments are in effect astrong pair of glasses that determine what we can see, bringing

some part of the world into sharp focus, at the expense of

blurring other parts.

Role I: A KR is a Surrogate


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Role I: A KR is a Surrogate

Any intelligent entity that wishes to reason about its worldencounters an important, inescapable fact: reasoning is aprocess that goes on internally, while most things it wishes toreason about exist only externally. A program (or person)engaged in planning the assembly of a bicycle, for instance,may have to reason about entities like wheels, chains,sprockets, handle bars, etc., yet such things exist only in theexternal world.

This unavoidable dichotomy is a fundamental rationale and rolefor a representation: it functions as a surrogate inside thereasoner, a stand-in for the things that exist in the world.Operations on and with representations substitute for operations

on the real thing, i.e., substitute for direct interaction with theworld. In this view reasoning itself is in part a surrogate foraction in the world, when we can not or do not (yet) want to takethat action. (1)

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