CIS4330: Professor KirsExpert Systems Slide 1 An Overview of Expert Systems.

CIS4330: Professor Kirs Expert Systems Slide 1

An Overview of Expert Systems


TOPICSTOPICS The nature of expertise

• Who is an Expert, and Why?

The Characteristics of an Expert Systems

• What Makes it different and Why ?

Additional Issues in Expert Systems

• Knowledge acquisition (Building knowledge bases)• Knowledge assessment• Explanation facilities


The Nature of Expertise Assumes a highly specialized

set of Skills• NOT just general knowledge

Assumes a very specialized problem domain

• Analogous to our previous ‘Forest vs. Tree’ Idea

Assumes logic, problem solving and experience

• NOT simple intuition or indefinable behaviors


The Nature of Expertise Who is an Expert??

• That is NOT an easy Question• There are many practitioner but

very few experts

Performance

Expertise

• Notice that just because you have experience, that does NOT mean that you are an expert

Characteristics of Experts• Fast, ACCURATE, problem Solving• Pattern Recognition• Use of Heuristics – Based on past

experience• Scarcity


The Nature of Expertise Necessary Expert Traits

• Be Recognized as an Expert• Know how they perform the task

• Have the time and ability to explain how they perform

• Can NOT just act intuitively without being able to explain their behaviors

• Be Motivated to Cooperate


The Nature of Expertise How do you know who is an expert??

• Also NOT an easy Question, although some are obvious

• There are references, However (a few off the Internet):• ExpertPages.com: A directory for legal professionals in search of

experts, expert witnesses, or consultants. Search by state, country, or subject area. http://www.expertpages.com/

• Experts Directory A searchable directory of experts from the legal, medical, journalism and other professions. http://www.experts.com

Are they really Experts ??? Don’t Mortgage the House!


Expert System Characteristics“An expert system is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice.” Jackson (1999)

Turing Test

1912-54

• A computer program demonstrates artificial intelligence if it can “pass’ as a human (c. 1950)

• In 1990, the Cambridge Center for Behavioral Studies began offering the $100,000 Loebner Prize to the first program whose responses were indistinguishable from a human’s

(No one has ever won)


Expert System Characteristics• Gary Kasparov vs. IBM’s Deep Blue

• May 11, 1997

• Garry Kasparov resigned 19 moves into Game 6

• Deep Blue wins the Best of Six game series 3.5 to 2.5

• IBM Development Team wins $700,000

• Kasparov wins $400,000

• The first win by a computer program over an International Grand Master since man/computer games were first began in 1970


Expert System Characteristics Basic Requirements

• simulates human reasoning

• Rule/Heuristic Based:

Rule: If there is a potato in the tailpipe, the car will not start.Finding: There is a potato in the tailpipe.Conclusion: The car will not start.

(Truth preserving inference)

Rule: If there is a potato in the tailpipe, the car will not start.Finding: My car will not start.Conclusion: Therefore, there is a potato in the tailpipe.

(Non-Truth preserving inference)



• simulates human reasoning • Inference Engines

• Reasons with any rule constructed via rule set manager

• Searches for applicable rules

• Evaluates the predicates of those rules to determine their “truth”

• Executes the actions specified in “fired” (activated) rules

• The ‘Driving’ Force in an Expert System




• Corresponds to the idea of Deductive reasoning

TheoryTheoryTheoryTheory

HypothesisHypothesisHypothesisHypothesis

ObservationObservationObservationObservation

ConfirmationConfirmationConfirmationConfirmation

• Forward Chaining

RejectionRejectionRejectionRejection

Birds can Fly

Ostriches Can Fly

(I Fly to Australia)

OK – I was wrong !




• Consists of a condition part and an action part

• Conditions (rules) are matched against the database

• The forward chaining engine cycles repeatedly until it runs out of rules or a rule instructs it to stop.

• If true, the action is fired

• Corresponds to the idea of Deductive reasoning

• Forward Chaining




ObservationObservationObservationObservation

PatternPatternPatternPattern

Tentative HypothesisTentative HypothesisTentative HypothesisTentative Hypothesis

TheoryTheoryTheoryTheory

• Corresponds to the idea of Inductive reasoning

• Forward Chaining• Backward Chaining

I’m back in The Australian Outback – Bird watching

Birds Flying, but no Ostriches

Ostriches Can’t Fly (what a Moron I was!)

Not all Birds can Fly



• simulates human reasoning

• Involves trying to prove a given goal by using rules to generate sub-goals and recursively trying to satisfy them.

• The engine looks at conclusions and determines all rules that could reach that conclusion

• Each rule is then examined for its premises

• If true, the rule is fired and a value is established

• The process continues until all possible solutions are generated

• Inference Engines

• Corresponds to the idea of Inductive reasoning

• Forward Chaining• Backward Chaining



• simulates human reasoning • Knowledge Representation

• A repository (Database) of data and metadata

• Contains all the Rules established by the manager

• Knowledge Bases

• The data are stored as objects, which can be fired as needed

• Includes Symbolic data

• Includes Relationships between data

• May be used in conjunction with a standard database



• simulates human reasoning • Knowledge Representation • Deal with realistically complex Problems • Reach Multiple Conclusions

• Especially as a result of backward chaining

• Explain the conclusions reached• The logic used must be demonstratable

• Deal with Missing Information• “Fuzzy Logic”• Non-numerical Analysis

• Demonstrate High Performance• Should approximate the performance of the

expert


Expert System Characteristics Basic Requirements ES Components

Inference Engine

User Interface

DatabaseKnowledge

Base

ES ShellA rule engine and

scripting Environment


Decision Support Systems Expert Systems


Differences Between ES and DSS

• Based On Expert • No Experts Available• Based on Logical Reasoning • Based on Numerical Analysis• System Questions User • User Questions System• Used Frequently • Used for Ad-hoc Problems

• Final Solution(s) Provided • Outputs provided based Analysis • Very Accurate • Unknown Accuracy • Multiple Solutions • Always the same output • Learning Possible • Always the same output

ES Components


Additional Topics Knowledge Acquisition

“The transfer and transformation of potential problem-solving expertise from some knowledge source to a program” - Buchanan et al. (1983)

• Transfer of the Expert’s Knowledge as a set of rules into the Knowledge Base

• Since the Expert is not expected to code the rules, a Knowledge Engineer is required• lengthy & intense interviews Required• slow (2 to 5 units of knowledge /day)

??? Why ??? • Imprecise, illogical, jargon or colloquialisms, experience, contextual detail, reliability of sources, ...



• Example: How to find a forgotten Password:

Expert (Computer Center Guru): Well, if it’s a YP pass-word, I first log on as root on the YP master

KE: (Knowledge Engineer): Er, what’s the YP master?

Expert: It’s the diskful machine that contains a database of network information

KE: ‘Diskful’ meaning - ?

Expert: -it has the OS installed on local disk

KE: Ah. (scribbles furiously) So you log on…

Expert: As root. Then I edit the password datafile, remove the encrypted entry, and make the new password map...

This is the weakest link in the process !!



• Potential Solutions/Problems• automated knowledge elicitation

• interactive programs/automated conversation • Problem: There are no Good Programs available (yet)

• textual scanning

• Parsing of conversations to extract the important components

• Problem: NLP is still in its infancy

• machine learning • deriving decision rules from examples

• Problem: Only Limited Success to date

I don’t get it !

Me Neither• evaluating / weighting rules • performance optimization of rules


Additional Topics Knowledge Acquisition Knowledge Assessment

• logical adequacy • sound & complete inferencing

• heuristic Power• efficiency Vs. optimality (Effectiveness)

• notational Convenience• How accurately do the rules reflect

the logic?


Additional Topics Knowledge Acquisition Knowledge Assessment Explanation Facility

• Necessary to check validity of Solutions

• The Chain of reasoning must be logged

• Solution Accountability must be determined

• Deficiencies must be corrected


Additional Topics Knowledge Acquisition Knowledge Assessment Explanation Facility

• LISP (LISt Processor)

• Prolog

• CLIPS (Free Download: http://www.ghg.net/clips/CLIPS.html)• Jess (Free Download: http://herzberg.ca.sandia.gov/jess/ )

Available Packages/Tools

• Others: A good list can be found at

http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/expert/systems/0.html

• Symbolic Manipulation Languages

• Expert Shells


????????????? Any Questions

(Please !!!) ?????????????


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CIS4330: Professor KirsExpert Systems Slide 1 An Overview of Expert Systems.

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