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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