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Transcript of 1 CSNB234 ARTIFICIAL INTELLIGENCE Chapter 7 Introduction to Expert Systems Chapter 7 Introduction to...
![Page 1: 1 CSNB234 ARTIFICIAL INTELLIGENCE Chapter 7 Introduction to Expert Systems Chapter 7 Introduction to Expert Systems Instructor: Alicia Tang Y. C. (Chapter.](https://reader030.fdocuments.net/reader030/viewer/2022032604/56649e5c5503460f94b53df9/html5/thumbnails/1.jpg)
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CSNB234CSNB234ARTIFICIAL INTELLIGENCEARTIFICIAL INTELLIGENCE
Chapter 7Introduction to Expert Systems
Chapter 7Introduction to Expert Systems
Instructor: Alicia Tang Y. C.
(Chapter 8, Textbook)(Chapter 3 & Chapter 6, Ref. #1)
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EXPERT SYSTEM (ES)EXPERT SYSTEM (ES)
Definition– ES is a set of computer programs
that can advise, consult, diagnose, explain, forecast, interpret, justify, learn, plan and many more tasks that require ‘intelligence’ to perform.
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An “expert system” is defined as
““A computerized clone of a human expert””
(Definition taken from Oxford Science Publication)
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EXPERT SYSTEMS: CHARACTERISTICSCHARACTERISTICS
– Perform at a level equivalent to that of a human expert.
– Highly domain specific.– Adequate response time– Can explain its reasoning.– It can propagate uncertainties and provide
alternate solutions through probabilistic reasoning or fuzzy rules .
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AN EXPERT AND A SHELL
EXPERT: An expert in a
particular field is a person who possess considerable knowledge of his area of expertise
ES SHELL A special purpose tool
designed for certain types of applications in which user supply only the knowledge base (e.g. EMYCIN)
It isolates knowledge-bases from reasoning engine
Hence software portability can be improved
Domain-specific
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Shell Concept for Building Expert Systems
KBe.g. rules
Consultation Manager
KB Editors& debugger
ExplanationProgram
KBMFInference Engine
shell
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ComparisonComparison (I)(I) Conventional Systems
– information & its processing are combined in one sequential program
– programs do not make mistake (but programmers do make it)
– the system operates only when it is completed
– execution is done on a step-by-step basis (i.e. it is algorithmic)
Expert Systems– knowledge base is separated
from the processing (inference) mechanism
– program may make mistake (we want it to make mistake!)
– explanation is part of most ES– the system can operate with
only a few rules (this is called fast prototyping)
– changes in the rules are easy to accomplish
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ComparisonComparison (II)(II) Conventional Systems
– changes in programs are tedious
– do not usually explain why or how conclusions were drawn
– need complete information to operate
– E__________ is a major goal
– easily deal with q_________ data
Expert Systems– can operate with
incomplete or uncertain information
– execution is done by using heuristic based on rules of logic
– E___________ is the major goal
– easily deal with q______ data
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RIGHT TASKS FOR RIGHT SYSTEMS
Facts that are knownExpertise available but is expensive
Analyzing large/diverse data– E.g. Production scheduling & planning, diagnosing and troubleshooting, etc.
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Generic Categories of Expert Systems (1)
Interpretation– inferring situation descriptions from
observationPrediction
– inferring likely consequences of given situations
Diagnosis– inferring system malfunctions from
observations
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Generic Categories of Expert Systems (2)
Design– configuring objects under constraints
Planning– developing plans to achieve goals
Repair– executing a plan to administer a
prescribed remedy
Other categories include: monitoring, debugging, control, instruction
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BENEFITS OF EXPERT SYSTEMS (I)
Expertise in a field is made available to many more people (even when human experts are not around in the company).
Top experts’ knowledge gets saved rather than being lost, when they retire or should they have resigned.
Facts are stored in a “Systematic” way. & Easy to keep on adding new knowledge on it
Allows human experts to handle more complex problems rapidly and reliably.
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Early EXPERT SYSTEMS (70s – mid 80s) MYCIN
– USES RULE-BASED SYSTEM– GOAL-DRIVEN– RULES INCORPORATED IN MYCIN REFLECTED UNCERTAINTY ASSOCIATED WITH KNOWLEDGE
– CERTAINTY FACTOR WAS USED TO DERIVE CONCLUSION DENTRAL
– WAS DEVELOPED IN STANFORD UNIVERSITY TO ANALYZE AND INTERPRET CHEMICALS AND THEIR MOLECULAR STRUCTURES
– DEVELOPERS INCLUDE JOSHUA LEDERBERG (NOBEL PROZE WINNER IN GENETICS)
– EXPERT’S “KNOW-HOW” ARE EXPRESSED IN RULES; RULE-OF-THUMB TECHNIQUE IS USED
PROSPECTOR– KBS TO INTERPRET GEOLOGIC DATA FOR MINERALS EXPLORATION
– INCOPORATED BAYES THEOREM (PROBABILISTIC REASONING APPROACH)
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Early EXPERT SYSTEMS (70s – mid 80s)
XCON–RULE-BASED SYSTEM, DATA-DRIVEN
REVEAL–FUZZY LOGIC USED
CENTAUR–RULES AND FRAMES-BASED SYSTEM
HEARSAY I – for speech recognition
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Characteristics common to Characteristics common to early ESearly ES
Could perform at a level equivalent to human experts
Large amount of domain specific knowledge
Rule-based systems: knowledge incorporated in the form of production rule
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Popular Expert Systems Application Domain
Electronics : helps in VLSI/ULSI design
Law : system serves as an auditor
Manufacturing : in production & process controls
Medicine : illness diagnosis
Chemistry : synthesis planning
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EXPERT SYSTEMS:LIMITATIONS
SYSTEMS ARE TOO SUPERFICIALRAPID DEGRADATION OF PERFORMANCEINTERFACES ARE STILL CRUDEINABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING– E.g. either forward or backward and not both
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The ResistorsThe Resistors
Domain expertsNon-expertsOther information technologists such as DBA, Network specialists
UsersManagementTroublemakers
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Consultation Environment(Use)
Development Environment(Knowledge Acquisition)
User Expert
User Interface
Inference Engine
ExplanationFacility
Working Memory
Facts ofthe Case
Recommendation,Explanation
Facts ofthe Case
KnowledgeEngineer
KnowledgeAcquisition
Facility
KnowledgeBase
Domain Knowledge(Elements of Knowledge Base)
STRUCTURE OF AN EXPERT SYSTEM
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Figure: Key components of an Expert Systems
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Explanation FacilityExplanation Facility
Why need it?– It provides sound reasoning besides quality result.
Common types– “How” a conclusion was reached– “Why” a particular question was asked
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Importance of ExplanationImportance of Explanation
It can influence the ultimate acceptance of an Expert System.
Use as a debugging tool.Use as a component of a tutoring system.
Who needs explanation?Our clients : to be convinced to
purchase.Knowledge Engineer: to check if all
specifications are met?
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Approaches Used (1)Approaches Used (1)
Canned Text– prepared in advance all questions and
answers as text– system finds explanation module and
displays the corresponding answer– problem:
difficult to secure consistency
– suitable for slow changing system only
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Paraphrase– Tree Traverse
to answer WHY– look up the tree
to answer HOW– look down the tree to see sub goals
that were satisfied to achieve the goal
Approaches Used (2)Approaches Used (2)
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Rule-based Systems
In expert system development, a tool is used to help us to make a task easier. The tool for machine thinking is the
Inference Engine.
Most expert systems are rule-based.
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FACTS AND RULES (revision)FACTS AND RULES (revision)
FACTS : A mammal is an animal A bird is an animal Arthur is a man Ben drives a car Catherine has blue eyes
RULES : If a person has RM1,000,000 then he is a
millionaire. If an animal builds a nest and lays eggs then
the animal is a bird.
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Rule 1: if you work hard and smartthen you will pass all examinations
Rule 2: if the food is goodthen give tips to the waiter
Rule 3: if a person has US1,000,000then he is a millionaire
Examples of rules:
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These are methods for deducing conclusions. The former predicts the
outcome (conclusion) from various factors (conditions) while the latter could be very useful in trying to determine the causes
once something has occurred.
Detailed description and working examples of rule-based systems and their
reasoning methods will be dealt separately in other chapters.
Forward Chaining and Backward Chaining
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Chaining SystemsChaining Systems
Forward– it predicts the
outcome from various factors (conditions)
Backward– it could be very
useful in trying to determine the cause (reason) once something has occurred
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InputData
Few Items(For Example, UserSpecifications fora Computer System)
Conclusion(Goals)
Many Possibilities(For Example, a ComputerConfiguration)
(a) Forward Chaining: IF - Part Matches Shown
Inference Strategies (I)
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InputData
Extensive;Much of the DataObtained by theSystem Queryingthe User (ForExample,Investor’s Profile)
Conclusion(Goals)
Few Possibilities(Known in Advance((For Example, Investment Options)
(b) Backward Chaining: THEN - Part Matches Shown
Inference Strategies (II)
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Exercise #1Exercise #1
You have seen what tasks are “just right” for ES and now you are
required to answer the following question:
– List a “Too hard” task for computers and explain briefly why they are considered ‘too
difficult’.
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Nice to know…Nice to know…
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RULE-BASED VALIDATION
There are essentially 5 types of inconsistency that may be identified, these are:– Redundant rules– Conflicting rule– Subsumed– Unnecessary Premise(IF) Clauses– Circular rules
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REDUNDANT RULES
Rule 1– IFA = X AND B= Y THEN C = Z
Rule 2– IF B=Y AND A=X THEN C=Z AND
D=WRule 1 is made redundant by rule 2.
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CONFLICTING RULES
Rule 1
–IF A = X AND B= Y THEN C = Z Rule 2
–IF A=X AND B=Y THEN C=W Rule 1 is subsumed by rule 2 thus
becomes unnecessary.
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SUBSUMED RULES
Rule 1
–if A = X AND B= Y THEN C = Z Rule 2
–if A=X THEN C=Z to be revised.
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UNNECESSARY PREMISE (IF) CLAUSES
Rule 1
–IF A = X AND B= Y THEN C = Z Rule 2
– IF A=X AND NOT B=Y THEN C=Z Remove B=Y and NOT B=Y to
have just one rule.
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CIRCULAR RULES
Rule 1– IF A = X THEN B = Y
Rule 2– IF B=Y AND C=Z THEN DECISION=YES
Rule 3 IF DECISION=YES THEN A = X
Restructure these rules !