& Expert Systems-General

8/6/2019 & Expert Systems-General

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Expert Systems

KR Chowdhary, Associate Professor,

Department of Computer Science & Engineering,MBM Engineering College, JNV University, Jodhpur,

[email protected]


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Defining Expert Systems

Definitions:

Where human expertise is needed to solve problems,expert systems are likely candidates to solve thoseproblems.

What is an expert system (ES)?

A set of programs that manipulate encodedknowledge to solve problems in a specialized domainthat normally require human expertise.

Encoded knowledge is used in inferencing orreasoning process.


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Uses knowledge rather than data for controlthe solution.

Knowledge is maintained separate from

control program ES are capable of explaining howand whya

particular solution arrived.

Uses symbolic representation of knowledge

and performs symbolic computation whichclosely matches human beings.

Uses meta-knowledge.

Characteristic features of ES:


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Applications of Expert Systems

Medical Diagnoses

Diagnosis of complex electronic andelectromechanical systems

Diagnosis of software development projects Weather forecasting

Forecasting crop damages

Identification of chemical compound

structures Location faults in computer and

communication systems


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Applications

Scheduling of Customer orders, jobs in OS, inproductions.

Evaluation of applications for loan, eligibility

criteria, etc. VLSI Design

Military applications, like those in battle fields

Law area, like-civil laws, disputes, etc.

Teaching AI techniques


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Why to use ES?

Commercial viability: whereas there may be only a few expertswhose time is expensive and rare, you can have many expertsystems

expert systems can be used anywhere, anytime

expert systems can have more knowledge than experts expert systems can explain their line of reasoning

Weaknesses:

expert systems are as sound as their KB; errors in rules meanerrors in diagnoses

automatic error correction, learning is difficult (although machinelearning research may change this)

little common sense reasoning: idiot servants


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Rule-based Expert Systems

These are most common type of ES

Uses the knowledge encoded in the form ofproduction rules (ifthen .. rules), i.e.

1. If cond1 and cond2 and cond3

Then take action-12. If temperature > 200 deg and water level low

Then open the safety valve

Each rule represents a small chunk of knowledgerelating the given domain

A number of rules may give a chain of inferencesstarting from some known facts to usefulconclusions.


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Input

Output

UserExplanatio

n Module

I/O

Interface

Editor

InferenceEngine CaseHistory

File

KB WorkingMemory

Learning

Module

Expert System


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Expert System..

User interface:

Question answer type

Menu-driven

Natural language

Graphic interface


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KB: Contains facts and rules about some specialized domain.

Building KB using editor.

Inference Process: Inference Engine accepts user input

queries in response to questions through I/O interface.Inference process is carried out by three stages: match, select,execute.

What is match?

contents of working memory are compared with KB. Whenmatch is found, the corresponding rules are placed in theconflict set (i.e. select). Further it needs instantiations toconfirm match. This follows execution.

Expert System..


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Inferencing

KB Working

Memory

1. Match

Conflict Set

2. Select

3. Execute

Inference Cycle


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Prolog uses backward chaining (goal driven strategy),matching of sub goals for the goal to be achieved.

Rules may be tested exhaustively or selectivelydepending on the control strategy.

The chaining continues as long as matches can befound between clauses in the working memory andthe rules in the KB.

The search can be limited to few hundred rules.

Uncertainty measures can also be used

Prolog rules:


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Explaining how&why?

How did you know that? (for inference result)

Why do you need to know that? (when additionalinformation is asked)

I/O Interface: Permits the user to communicatewith the system in a more natural may by permittingthe the use of simple selection menus or use of a

restricted natural language.

Explanation


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Explanation..

When you are asking why a particular action wasperformed at a node in the tree... how questions: recite the children

why questions: recite the parent

thus backward chaining expert systems naturallysupport explanation can be done during inference: when user asked for data,

s/he can as why and a reason is given

after expert advice given, user can ask how, why, andeven why not (ie. why did some rule fail)

a great tool for KB debugging! forward chaining: explanation not as structured;


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Knowledge Acquisition andvalidation

This is also called knowledge engineeringprocess

Needs acquiring of knowledge and thenencoding it

Knowledge is derived from experts, journals,texts, reports, interviews, etc.

Job is performed by knowledge engineer.

Consolidating of the knowledge may needcollective efforts of many persons.


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Knowledge acquisition process

Domain

Expert KBSystem

Editor

Knowledge

Engineer


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1. Medical expert systemsMedical expert systems are an active area.

Humanitarian uses: remote areas can have world-classexpertise,

medical science is complex; expert systems are intelligent

advisors and assistants MYCIN

diagnose bacterial infections

uses certainty factors: ranked diagnoses are generated

EMYCIN: empty MYCIN - the shell of MYCIN for use in otherapplications

CADUCEUS: expert system with entire internal medicine KB

its been active since 1970s

Examples expert systems


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2. XCON configures VAX systems from user requirements

DEC uses it; has increased throughput of systemdesign

10,000 rules said to have replaced X system designers with 4X

expert system maintainers!

3. DELTA/CATS diesel electric locomotive trouble shooter

problem: system maintenance



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4. Jonathans Wave commodities trading

incorporates several experts approaches

runs in Prolog and C

lots of AI groups on Wall Street!

5. DENDRAL(1960): For determining the structure of chemicalcompounds given its specification.

6. PROSPECTOR:Assists the geologists in discovering theminerals deposits.



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7. Codecheck expert system to evaluate C source code

complexity, formatting, standards and portability adherence

identifies overly complex code (prime source of programproblems!)

8. CHORAL expert system for harmonizing chorales in the style of J.S.

Bach

similar system: harmonic analysis of tonal music

rules obtained from: studying composers music,

musicologists some systems used for composition (with interesting results)



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Expert systems are now ubiquitous (I.e.,present every where)

still lots of active research:

probabilistic knowledge and deduction

large system maintenance

knowledge acquisition popular domains

industrial, financial, medical

legal: interpret laws by the book

education: intelligent tutoring

Some domains remain difficult to implement those requiring artistic creativity and skill

those requiring common sense

The state of the art


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The need for a solution must justify the costsinvolved in development

Human expertise is not available in all situationswhere it is needed

The problem may be solved using symbolic reasoningtechniques

The problem is well structured and does not require(much) common sense knowledge

The problem cannot be easily solved using more

traditional computing methods Cooperative and articulate experts exist

The problem is of proper size and scope

Steps in expert system building:Choosing Problem:


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expert system building is a software engineeringtask, but with a twist: knowledge base KB will be continually refined, corrected, updated

expert system design & implementation steps

1. initial proposal - identification of problem, expert(s), benefits2. create a prototype

3. knowledge engineering: interview expert for many weeks(months)

4. implement experts expertise in KB

5. test expert system; go to 3 (let expert see results)

6. put in production ; refine errors when found

Steps in expert system building


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Knowledge engineering: the extraction of knowledge from anexpert, and encoding it into machine-inferrable form

K.E. is the most difficult part of expert system implementation

the knowledge engineer must extract detailed info from anexpert; but s/he is not expected to understand it

the expert relies on years of experience and intuition: askingthem to deconstruct knowledge is difficult and frustrating

can also be political factors

many strategies for knowledge engineering

multiple KEs

multiple experts

watch expert in the field

continual expert feedback on expert system performance

Knowledge engineering


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Other types of expert system

Called Non-production systems

These are:

Semantic Network based

Frame based

Decision tree based

Neural net based


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Problems of using expertsystems

Choice of domain

Acceptability

Uncertainty

Updating Limitation (ES do not know itself)

Testing

Behavior

Knowledge acquisition


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Advantages of using expertSystems

Availability

Consistency

Comprehensiveness (detailedknowledge)

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