knowledge base systems (1)

7/30/2019 Knowledge Base Systems (1)

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Knowledge Base Systems

production rule representation.


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Production Systems

Introduction

These are a form of knowledge representationwhich have found widespread application in AIparticularly in the area of Expert Systems.

Production systems consist of three parts:

a) A rule base consisting of a set of productionrules.

b) the data c) an interpreter which controls the systems

activity.


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Production Rules

A production rule can be thought of as a

condition action pair. They take the form

IF condition holds THEN do action e.g.

IF traffic light is red THEN stop car.


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Firing Production Rules

A production rule whose conditions aresatisfied can fire, i.e. the associatedactions can be performed.

Conditions are satisfied or not according towhat data is currently available.

Existing data may be modified as

production rules are fired. Changes in datacan lead to new conditions being satisfied.

New production rules may then be fired.


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The decision which production rule to fire

next is taken by a program known as the

interpreter.

The interpreter therefore controls the

systems decisions and actions and must

know how to do this.


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Production Systems

Production systems were first proposed in

1943 by Post.

Present day systems however bear little

resemblance to those earlier ones.

Newell and Simon in 1972 used the idea

of production systems for their models of

human cognition and AI in general has

found widespread use for them.


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Suitable Domains

Davis and King (1977) proposed the following criteria forproblem domains where productions rules are suitable.

1: Domains in which knowledge is diffuse, consisting ofmany different strands of knowledge rather than some

domain which is based on a unified theory like Physics. 2: Domains in which processes cane be represented as

a set of independent actions as opposed to domains withdependent subprocesses.

3: Domains in which knowledge can be easily separatedfrom the manner in which it is to be used as opposed tocases where representation and control are merged.


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Knowledge Base Systems

Knowledge Base systems are intended to

perform tasks which require some

specialized knowledge and reasoning.

Medical diagnosis, geological analysis,

and chemical compound identification are

examples of tasks to which Knowledge

Base systems have been applied.


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Expert Systems

Knowledge Base systems are often called

expert systems because the problems in

their application domain are usually solved

by human experts.

For example medical diagnosis is usually

performed by a doctor.


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Parts of a Knowledge Base System

Knowledge Base systems consist of Four

major parts;

The Knowledge Base,

The Inference Engine

The User Interface and

The Explainer Knowledge Acquisition Module


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Inference Engine

AgendaKnowledge Base

(rules)

Explanation

Facility

User Interface

Knowledge

Acquisition

Facility

Working Memory

(facts)


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The knowledge to which the Knowledge Basesystem has access, is stored in the KnowledgeBase, (hence the name).

The Inference Engine is the part of a KnowledgeBase system which is responsible for using itsknowledge in a productive way.

The Knowledge Base system's reasoning

mechanisms are built into the Inference Engine.Most Knowledge Base systems employdeductive reasoning mechanisms.


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The Knowledge Base system communicates

with the user through the User Interface.

In many applications the Knowledge Base

system is required to explain its reasoning to theuser. This is particularly true in situations such

as the identification of chemical structures where

new results must be verified.

The Explainer is that part of the Expert System

which provides explanation and verification


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Knowledge Acquisition Modules

These help with acquiring the systems

knowledge.

They do a variety of tasks for example in somesystems they provide system analysis facilities

similar to those of database development tools

Mostly they help encode the knowledge from ahigh level format into a computer usable

representation.


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System Architecture


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Inference Engine

AgendaKnowledge Base

(rules)

Explanation

Facility

User Interface

Knowledge

Acquisition

Facility

Working Memory

(facts)


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Obviously the knowledge to which a

Knowledge Base system has access is no

good to it, if it cannot use it to solve the

problems in the application domain.

Therefore the systems knowledge must be

represented in a form which can be

manipulated by the reasoningmechanisms of the Inference Engine.


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While the Knowledge Base, the IE and the UserInterface are essential components, in many KnowledgeBase systems there are also facilities to help in theacquisition of new knowledge.

Teiresias, [DAVIS '76], which is used in association withMYCIN, [Shortliffe '76, Davis '76] is an example of sucha system.

It elicits high level information from the user which itconverts into structured knowledge for its KnowledgeBase. It also performs consistency checks on the

updated knowledge base. The ability to acquire new knowledge is important since

the amount of knowledge to which a system has accessdetermines the range of problems which it can solve.


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Applications of Knowledge Base

systems

Knowledge Base systems have been applied to manydiverse problem domains, such as the following.

Diagnostic Aids such as MYCIN, [Shortliffe '76, Davis

'76], which diagnoses bacterial blood infections andPUFF, [Kunz et al '78], which diagnose pulmonarydisorders.

MYCIN was a joint venture between Dept. of ComputerScience and the Medical School of Stanford University.

Much of the work took place in the 1970's. Mycin was designed to solve the problem of diagnosingand recommending treatments for meningitis andbacteremia, (blood infections).


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Aids to Design and Manufacture such asR1, [McDermott '82], which configurescomputers.

Teaching Aids such as SCHOLAR[Carbonell '70] which gives GeographyTutorials and SOPHIE, [Brown et al '82],which teaches how to detect breakdown inelectrical circuits.

Problem Solving


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Recognition of forms, e.g. DENDRAL,

[Buchanan and Feigenbaum '78, Lindsay et al

'80], which recognizes the structures of

chemical compounds. Robotics e.g. SHDRLU, [Winograd '73], which

manipulates polygons in a restricted

environment.

Game playing systems such as Waterman's

Poker Player, [Waterman '70], and


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Automatic theorem Provers such as AM, [Lenat'82].

[Hayes-Roth et al '83, Handbook A.I. '82,

Waterman '86], describe some more categoriesthan those mentioned above. These includePlanning systems such as NOAH, [Sacerdoti'75] and MOLGEN, [Friedland '75] and

Prediction systems such as Political ForecastingSystems, [Schrodt '86] based on the HollandClassifier, [Holland '86].


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Rule-Based Expert Systems

Work with

a set offacts describing the current

world state

a set ofrules describing the expert

knowledge

inference mechanisms for combining

facts and rules in reasoning


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Architecture of Rule-Based XPS

1Knowledge-Base / Rule-Base stores expert knowledge as condition-action-

rules (or: if-then- or premise-consequence-rules)

objects or frame structures are often used torepresent concepts in the domain of expertise,e.g. club in the golf domain.

Working Memory

stores initial facts and generated facts derived bythe inference engine

additional parameters like the degree of trust inthe truth of a fact or a rule ( certainty factors) or

probabilistic measurements can be added


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Knowledge Engineering

Knowledge engineering is a general term

for the processes involved in building

expert systems: planning, knowledge

acquisition, system building,systeminstallation, system maintenance.

In the following notes "KE" stands for

knowledge engineer, and "DE" stands fordomain expert.

A hit t f R l B d


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Architecture of Rule-Based

Systems

Inference Engine

matches condition-part of rules against facts

stored in Working Memory (pattern matching);

rules with satisfied condition are active rules andare placed on the agenda;

among the active rules on the agenda, one is

selected (see conflict resolution, priorities of

rules) as next rule for execution (firing) consequence of rule can add new facts to

Working Memory, modify facts, retract facts, and

more


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Rule-Based Systems- Example Grades -

Rules to determine grade

1. If study then get good_grade

2. If do not_study then get bad_grade

3. If sun_shines THEN go_out

4. If go_out then do not_study

5. If stay_home then study

6. If awful_weather then stay_home


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Questions

Ask the following questions of the expert

System

Q1: If the weather is awful, do you get a

good or bad grade?

Q2: When do you get a good grade?


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forward reasoning rule chain

given fact: awful_weather 6,5,1

backward reasoning

hypothesis/goal: good_grade 1,5,6

Answer Question 1 Good Grade

Exercise Answer question 2 with forwardand backward chaining


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Explaining

Note we presented a rule chain when we solved

this problem.

If we asked How do you know we get a good

grade in awful weather we can say that By rule 6 if the weather is awful stay at home

By rule 5 If stay at home then you will study

And finally By rule 1 if you study you get a goodgrade


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In other words

In order to explain how we arrive at a

solution we list the chain of rules that were

fired on rout to this conclusion.

This is the basis of expert system

explainers


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Knowledge Acquisition

Obtaining knowledge for use in the

knowledge base of an expert system.


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Sources of knowledge:

Documents: textbooks, journal articles,technical reports, case histories, etc.

This will almost never be sufficient to

provide the knowledge base for a real-world expert system.

The range of problems which a textbook

examines and solves is always smallerthan the range of problems that a humanexpert is master of.


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Knowledge Analysis

Simultaneously with the knowledgeacquisition process, a knowledge analysisprocess takes place.

The KE uses the data from the knowledgeacquisition sessions to build a good modelof the expertise that the DE is using tosolve problems in the domain. This may ornot rely heavily on building a prototype -see below.


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Knowledge Elicitation

The most important branch of knowledgeacquisition is knowledge elicitation - obtainingknowledge from a human expert (or humanexperts) for use in an expert system.

Knowledge elicitation is difficult. This is theprinciple reason why expert systems have notbecome more widespread - the knowledgeelicitation bottleneck.

It is necessary to find out what the expert(s)know, and how they use their knowledge..


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Human experts

Expert knowledge includes:

domain-related facts & principles;

modes of reasoning; reasoning strategies;

explanations and justifications.


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Knowledge elicitation and experts

The knowledge elicitation (and analysis) task involves:Finding at least one expert in the domain who:

is willing to provide his/her knowledge;

has the time to provide his/her knowledge;

is able to provide his/her knowledge. Repeated interviews with the expert(s), plus task

analysis, concept sorting, etc, etc..

Knowledge structuring: converting the raw data (takenfrom the expert) into intermediate representations, priorto building a working system.


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Knowledge Elicitation continued

Building a model of the knowledge derived fromthe expert, for the expert to criticise. From thenon, the development proceeds by stepwiserefinement.

One major obstacle to knowledge elicitation:experts cannot easily describe all they knowabout their subject.

They do not necessarily have much insight intothe methods they use to solve problems.

Their knowledge is "compiled" (c.f. a compiledcomputer program fast & efficient, butunreadable).


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Techniques used in Knowledge

ElicitationVarious different forms of interview:

Unstructured. A general discussion of the domain,designed to provide a list of topics and concepts.

Structured. Concerned with a particular concept within

the domain.

Problem-solving.

The expert is provided with a real-life problem, of a kind

that they deal with during their working life, and asked tosolve it. As they do so, they are required to describeeach step, and their reasons for doing what they do. Thetranscript of their verbal account is called a protocol.


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Think-aloud.As above, but the expert merely imagines thatthey are solving the problem presented to them,rather than actually doing it. Once again, they

describe the steps involved in solving theproblem.

Dialogue.

The expert interacts with a client, in the way thatthey would normally do during their normal workroutine.


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Review.

It is standard practice to tape record KE

sessions. However, KEs should be aware

of the costs this involves, in time and

money.

The KE and DE examine the record of on of

the sessions described above, together.


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More Methods

Sample lecture preparation. The expertprepares a lecture, and the KE analysesits content.

Concept sorting ("card sort"). Questionnaires. Especially useful when

the knowledge is to be elicited from

several different experts. Repertory grid (particularly the "ladderedgrid" technique).


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Computerised knowledge

elicitation.

The state of the art in AI (especially NLP) is not

sufficiently advanced to permit fully-automated

knowledge elicitation. However, 'knowledge elicitation workbenches', or

'knowledge engineering environments', are commercially

available (e.g KEE, KnAcqTools); their principle use is to

simplify the task of converting a protocol into frames,rules, etc., and inserting these structures into an expert

system shell as soon as they are formulated.