1 Backward-Chaining Rule-Based Systems Elnaz Nouri December 2007.
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Transcript of 1 Backward-Chaining Rule-Based Systems Elnaz Nouri December 2007.
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Backward-Chaining Rule-Based Systems
Elnaz NouriDecember 2007
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Chapter 7 overview:
Introduction Medical consultation systems
Example 1:Meningitis Diagnosis Expert System Example 2:Meningitis Prescription Expert System Example 3:Alternative Prescription Expert System
Automobile Diagnostic System Example 4:Automobile Diagnostic via a Blackboard
Summary
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Introduction
The principle objective of backward chaining is to prove some goal or hypothesis
The process begins by collecting GOAL RULES Goal Rules contain the goal proven in their
THEN part The premise of the goal rules may be supported
by other rules, so they are set as Sub-Goals The inference engine searches through the
system’s rules in a recursive fashion.
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Backward Chaining inference engine will reach some premise that is not supported by any of the system’s rules (a Primitive) Ask User
The answer is placed in the current memory
The process continue until all goals and sub-goals are searched memory contains all information provided by user and inferred by rules.
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Medical Consultation Systems
Like MYCIN , performs diagnosis for infectious blood diseases
Unlike MYCIN , doesn’t identify organ but rather the nature of the infection
3 different expert systems: Diagnosis, Prescription, Prescription Changes
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Design Suggestion
Divide Complex problems into smaller tasks and design system for each task.
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Example 1: Megningitis Diagnosis Expert System
The system has only one goal :Prove or disprove “Infection is meningitis”
One Goal rule : RULE 1This can be proven if the user already
knows that the patient has meningitis or the system can infer it.
RULE 2,3,4 : search the test results RULE 5 : searches the area of patient
symptoms
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Meningitis diagnosis rules
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Medical Diagnosis Example Session
Begins with empty working memory
STEP 1: Find rules with hypothesis in “THEN” part
RULE 1
STEP 2: see if first premise in RULE 1 is listed in working memory
NO
STEP 3: see if this premise exists in “THEN” part of any rule
NO
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STEP 4: This premise is a primitive Question : “Do you know if patient has
Meningitis ?” Answer : NO Working Memory: Patient known to have
Meningitis - FALSE
STEP 5: look at second premise in RULE 1 and see if it’s in working memory
NO
STEP 6: see if this premise exists in “THEN” part of any rule
RULE 2
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STEP 7: see if first premise in RULE 2 is listed in working memory
NO
STEP 8: see if this premise exists in “THEN” part of any rule
RULE 3
STEP 9: Q: Were test run? USER: YES
STEP 10: Q: Were cultures seen? USER:YES
STEP 11: Q: The appearance of the culture is coccus?
USER: WHY System: This will aid in determining if cultures look like
Meningitis
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STEP 11: Q: The appearance of the culture is coccus?
USER: WHY System: This will aid in determining if cultures
look like meningitisRULE 4
STEP 12: USER : WHY 4.0 System: This will determining if “we suspect
meningitis from test results”RULE 3
STEP 13: System: The appearance of the culture is
coccus? USER: NO
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Working Memory
Patient known to have meningitis-FALSE Tests Run-TRUE Cultures Seen-TRUE Appearance of cultures is coccus-FALSE
The system was unsuccessful in establishing meningitis from test results
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STEP 14: look at second premise in RULE 2 and see if it’s in working memory
NO
STEP 15: see if this premise exists in “THEN” part of any rule
RULE 5
STEP 16: All premises of RULE 5 are unknown and primitives Questions
SYSTEM: Has the patient been suffering persistent headaches?
USER: YES
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SYSTEM: Has the patient been suffering dizziness?
USER: YES SYSTEM: Has the patient been lethargic? USER: YES
STEP 17: RULE 5 fires RULE 2 fires RULE 1(Goal Rule) fires
SYSTEM: “After considering your info, I believe the infection is Menningitis.”
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Example Review
Simple Goal Simple Questions Depth First Search Intelligent User Safety Net Ease of Expansion Documenting of Rules Inference Network
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Inference network
Graphical representation of the systems’ rules with the premises and conclusions of the rules drawn as nodes and their supporting relationships drawn as links
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Example 2: Meningitis Prescription Expert System
Prescription task: formulate action needed to correct the fault
Problem Solving Approach : - The rules are shown in next slide.- We have one goal to prove : “Prescription is ? Prescription”- 2 goal rules : RULES 1 ( higher priority )
and 2- RULE 3 : patient’s age
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Meningitis prescription rules
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Medical Prescription Example Session
The fact that patient has meningitis is knownGOAL : Determine proper series of drugs
STEP 1: Find rules with hypothesis in “THEN” part RULE 1 and RULE 2
STEP 2: chose RULE 1 because of higher priority and see if first premise in RULE 1 is listed in working memory
YES
STEP 3: see if the second premise of RULE 1 exists in working memory
NO
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STEP 4: see if this premise exists in “THEN” part of any rule
RULE 3
STEP 5: see if this premise of RULE 3 is listed in working memory
NO
STEP 6: see if this premise exists in “THEN” part of any rule
NO
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STEP 7: This premise is a primitive Question : “What is the patient’s age?” Answer : 8
STEP 8: Rule 3 fires (patient is a child) Rule 1 also fires
SYSTEM : “My first recommendation is this :Ampicillin --- Dose :
3.8 gGentamicin --- Dose :
131 mg ”
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STEP 9: USER: HOW
SYSTEM : To drive goal ”Prescription is ? Prescription” I considered:
-RULE1(1.1 is known to be true)-To drive 1.2 I considered RULE3-To drive 3.1 I asked you. your answer was 8.-RULE 3 was concluded.-RULE 1 was concluded.
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Example Review
Variable goal Rule priorities Accommodating user Numeric Relationships
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Example 3: Alternative Prescription Expert System
Non-Monotonic Reasoning : facts that are logically dependent on other facts that have been retracted can be retracted.
Database Access: - Using existing knowledge- Using general knowledge
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Prescription change rules
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Prescription change functions
Display current drugs Determine deleted drugs Determine substituted drugs Determine if drug is effective Determine if drug is safe Compare ages Try another drug Remember facts Recycle
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Alternative Prescription Example Session
Drug recommendation AMPICILIN-TRUE Drug recommendation GENTAMICIN-TRUE
GOAL: allow user choose alternative drug SYSTEM: do you want to consider another
prescription?USER: YES
SYSTEM: which one do you want to delete?USER: ampicilin
SYSTEM: what drug do you want to add?USER: stremptomycin
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SYSTEM: The requested drug comes from class AMINOGLYCOSIDES and would not be effective. you could try another drug.USER: PENICILIN
SYSTEM: The requested drug comes from class PENNICILLINS and would be an effective replacement for AMPICILLIN which also comes from PENNICILLINS.
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Example Review
Ease of modification General knowledge Monotonic Reasoning Non-Monotonic Reasoning
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Automobile Diagnosis System
Design Suggestion: For problems that require expertise from more than one individual, develop separate expert systems that capture the expertise of each individual.
Blackboard: The mean for infromation exchange when the ES is designed to incorporate separate expert modules.
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Example 4: Automobile Diagnostics Expert System
Object Attribute Value: allows you to display to user a menu of possible values.
Problem Solving Approach : - Determine problem area- Determine specific fault- Determine cost
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Automobile Diagnostics Example Session
SYSTEM: please tell me whether the engine WILL START,WON’T START?
USER: won’t start SYSTEM: please tell me whether the starter
DOES TURN,DOESN’T TURN?
USER: does turn SYSTEM: the engine turns VERY SLOWLY,
NORMALLY BUT WON’T START?
USER: normally but won’t start
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SYSTEM: I believe the problem might be with the FUEL SYSTEM. I will check with my expert in this area.
SYSTEM: The fuel gage DOESN’T MOVE,DOES MOVE?
USER: does move
SYSTEM: The car will START OCCASIONALLY, NEVER STARTS?
USER: star occasionally
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SYSTEM: After working on your problem it is my belief that your car has problem with the FUEL SYSTEM.The specific problem is DIRTY CARBURETOR.My recommendation is REBUILD THE CARBURETOR.The cost for such repair is 100 dollars.
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Example Review
Separate Systems Meta Rules Display of intermediate findings Blackboard O-A-V facts Initializing knowledge Intelligent Safety Net
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Summary on Backward Chaining Expert Systems
BC attempts to prove a goal by recursively moving back through the rules in search of supporting evidence
To ease the development and maintenance of these systems, design them in modular form
Care should be given to provide clear final displays and keeping user informed
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The user should allow user to provide known information and avoid unnecessary search by the system
Some intelligent findings should be provided even if the system is not totally successful
Database information should be used Cooperating expert systems modules
communicate over a structure known as Blackboard