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Backward-Chaining Rule-Based Systems. Elnaz Nouri December 2007. 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

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backward chaining rule based systems

Backward-Chaining Rule-Based Systems

Elnaz Nouri

December 2007

chapter 7 overview
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
introduction
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.
slide4
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.
medical consultation systems
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
design suggestion
Design Suggestion
  • Divide Complex problems into smaller tasks and design system for each task.
example 1 megningitis diagnosis expert system
Example 1: Megningitis Diagnosis Expert System
  • The system has only one goal :

Prove or disprove “Infection is meningitis”

  • One Goal rule : RULE 1

This 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
medical diagnosis example session
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
slide10
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
slide11
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
slide12
STEP 11: Q: The appearance of the culture is coccus?

USER: WHY

          • System: This will aid in determining if cultures look like meningitis

RULE 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
working memory
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
slide14
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
slide15
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.”

example review
Example Review
  • Simple Goal
  • Simple Questions
  • Depth First Search
  • Intelligent User
  • Safety Net
  • Ease of Expansion
  • Documenting of Rules
  • Inference Network
inference network
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
example 2 m eningitis prescription expert system
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
medical prescription example session
Medical Prescription Example Session
    • The fact that patient has meningitis is known

GOAL : 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
slide22
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
slide23
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 g

Gentamicin --- Dose : 131 mg ”

slide24
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.

example review1
Example Review
  • Variable goal
  • Rule priorities
  • Accommodating user
  • Numeric Relationships
example 3 alternative prescription expert system
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

prescription change functions
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
alternative prescription example session
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

slide30
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.
example review2
Example Review
  • Ease of modification
  • General knowledge
  • Monotonic Reasoning
  • Non-Monotonic Reasoning
automobile diagnosis system
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.
example 4 automobile diagnostics expert system
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
automobile diagnostics example session
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

slide36
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

slide37
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.

example review3
Example Review
  • Separate Systems
  • Meta Rules
  • Display of intermediate findings
  • Blackboard
  • O-A-V facts
  • Initializing knowledge
  • Intelligent Safety Net
summary on backward chaining expert systems
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
slide40
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
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