View
219
Download
1
Category
Preview:
DESCRIPTION
3 An expert system is defined as “ a computerized clone of a human expert ” From Oxford Science Publication
Citation preview
CCSB354CCSB354ARTIFICIAL INTELLIGENCEARTIFICIAL INTELLIGENCE
Chapter 8Introduction to Expert Systems
Instructor: Alicia Tang Y. C.
(Chapter 8, Textbook)(Chapter 3 & Chapter 6, Ref. #1)
2
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.
3
An expert system is defined asAn expert system is defined as
““a computerized clone of a human expert”a computerized clone of a human expert”
From Oxford Science Publication
4
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 .
5
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
6
Shell Concept for Building Expert Systems
KBe.g. rules
Consultation Manager
KB Editors& debugger
ExplanationProgram
KBMF Inference Engine
shell
7
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 ( )
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 ( )– changes in the rules are easy
to accomplish
8
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 h_________ and logic
– E___________ is the major goal
– easily deal with q______ data
9
RIGHT TASKS FOR RIGHT SYSTEMS
Facts that are knownExpertise available but is expensive
Analyzing large/diverse dataE.g. Production scheduling & planning, diagnosing and troubleshooting, etc. (will see them later on)
10
Generic Categories of Expert Systems (1)
Interpretation– inferring situation descriptions from
observationPrediction
– inferring likely consequences of given situations
Diagnosis– inferring system malfunctions from
observations
11
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
Others are: monitoring, debugging, control, instruction
12
BENEFITS OF EXPERT SYSTEMS (I)
Expertise in a field is made available to many more people (even when human expert is not present).
Top experts’ knowledge gets saved rather than being lost, when they retire
“Systematic”; no factors forgotten. Easy to keep on adding new knowledge Allows human experts to handle more complex
problems rapidly and reliably.
13
EXAMPLES of EXPERT SYSTEMS MYCIN
– USES RULE-BASED SYSTEM, GOAL-DRIVEN– EMPLOYED CF TO DERIVE CONCLUSION
PROSPECTOR– INCOPORATED BAYES THEOREM (PROBABILITY)– Interpret geologic data for minerals
XCON– RULE-BASED SYSTEM, DATA-DRIVEN
REVEAL– FUZZY LOGIC USED
CENTAUR– RULES AND FRAMES-BASED SYSTEM
DENTRAL – interpret molecular structure HEARSAY I – for speech recognition
14
LIMITATIONS
SYSTEMS ARE TOO SUPERFICIALRAPID DEGRADATION OF PERFORMANCEINTERFACES ARE STILL CRUDEINABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING (in most cases)
15
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
16
Figure: Key components of an Expert Systems
17
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
18
Importance of ExplanationImportance of ExplanationIt can influence the ultimate a________ of
an Expert System.Use as a d______________ tool.Use as a component of a tutoring system.
Who needs explanation?Clients : To be convinced.Knowledge Engineer: Specifications all
met?
19
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
20
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)
21
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.
22
FACTS AND RULESFACTS AND RULES
FACTS : A mammal is an animal A bird is an animal Adam is a man Ben drives a car
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.
23
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:
24
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
25
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
26
InputData
Conclusion(Goals)
Many Possibilities
(a) Forward Chaining
Inference Strategies (I)
27
InputData
Conclusion(Goals)
Few Possibilities
(b) Backward Chaining
Inference Strategies (II)
28
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 said too difficult.
And, why?
29
For your information…For your information…supplementary topicsupplementary topic
30
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
31
REDUNDANT RULESRule 1
– IFA = X AND B= Y THEN C = ZRule 2
– IF B=Y AND A=X THEN C=Z AND D=W
Rule 1 is made redundant by rule 2.
32
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.
33
SUBSUMED RULES
Rule 1–if A = X AND B= Y THEN C = Z
Rule 2–if A=X THEN C=Z
to be revised.
34
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.
35
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 !
Recommended