Knowledge-based Systems

March 1999 Dip HI KBS 1

Knowledge-based Systems

Alternatives to Rules


Knowledge-based Systems

• Rule-based– heuristic (expert) knoweldge encoded in rules.

• Model-based– reasoning is based on a model of a

device/system.

• Case-based– knowledge is provided by many examples of

solutions to previous cases.


Problems with Rules

• Fail to work if problem is not anticipated by rules.

• Heuristic rules can be applied inappropriately if some condition is omitted.

• With some understanding of the problematic system these inadequacies could be overcome.


Model-based Reasoning

• Just as experts revert to first principles when confronted with new or difficult problems…

• Model-based reasoners are based on a representation of the structure and behaviour of the system under analysis.

• Used especially in diagnosis of equipment malfunctions.


MBR : Diagnosis

• Simulate behaviour of components of device/system.

• Represent component interactions.• Represent known failure modes of components

and interconnections.• Compare actual device performance with that

predicted by the model.• If there is a discrepancy, reason about what

failures could account for observed bahaviour.


MBR Example

MULT-1

MULT-2

MULT-3

ADD-1

ADD-2

A=3

B=3

E=3

C=2

D=2

(F=12)

(G=10)

Actual F is 10

Predicted outputs

Fig 6.14 of Luger and Stubblefield, Third Edition.


Reasoning phase

• Generate hypotheses– either ADD-1, MULT-1 or MULT-2 is faulty

• Test each hypothesis– find MULT-2 appears to be OK (since ADD-

2’s output is good).

• Discriminate between surviving hypotheses with further observations.– E.g. check the actual output of MULT-1.


Problems with MBR

• Intensive knowledge acquisition.

• Requires an explicit domain model, a well-defined theory.– Excludes some medical specialties, financial

applications, ...

• Complex and detailed reasoning, slow?.

• Ignores (possibly valuable) experiential knowledge.


Problems cont/

• Can only handle problems explained by the model.– A model is a representation of some reality. It

leaves out many aspects. If the things that left out are the cause of the problem, the MBR won’t work.


Advantages of MBR

• More robust and flexible reasoning

• Can provide causal explanations. May serve a tutorial role.

• Knowledge may be transferable to related tasks.


Case-based Reasoning

• Rules and models may be difficult to devise for natural domains (e.g. medicine).

• In CBR “knowledge” is held in a case base of real prior problems and their solutions.

• Case-based diagnosis is common– physician matches new case with one seen

previously and uses the diagnosis of the old case as a starting point.


Application domains

• Technical support help desks

• Classification type problems– see Machine Learning lecture

• Case-based design

• Fraud detection

• Legal planning– much law is precedent (case) based


Components

• Representation• Retrieval

– Matching engine retrieves cases similar to target case.

• Adaptation• Remembering

Spec

Soln?

T1

MatchingEngine

Target

Case Base

Spec

Soln

B125

Spec

Soln

B127

Spec

Soln

B125

Spec

Soln

B103


Breathalyser

Gender

FrameSize

Amount

Meal

Duration

Male

1

1

snack

60

BAC 0.2

N-1

Gender

FrameSize

Amount

Meal

Duration

Female

4

4

full

90

BAC 0.8

N-3

Gender

FrameSize

Amount

Meal

Duration

Male

1

3

snack

120

BAC 0.7

N-55

Example cases

• Duration is duration of drinking session.• Perhaps elapsed time should be added as a

case feature?


Case Representation

• The knowledge engineering task is focused on deciding how to represent cases– what features best characterise cases

• i.e. predictive features

– may require expert analysis• e.g. for image classification the bitmap may need to

be converted to an edge map.

• e.g. height and weight may not be useful in themselves for classifying apples and pears,but height/weight ratio is.


Case retrieval

• Based on some similarity measure.– e.g number of matching features– e.g. distance measure based on difference

between numeric features

• Indexes may be used to speed the retrieval


Case indexing - Example

Location: B-Rooms: Age: Rec-Rooms: Kitchen: Rear-Acc.:

Tot-Area: En-Suite: : :

SM-1 3 Modern 2 Large Yes

>1,200 Yes : :

Price £98,000

Indices3 LR4WF

Location: B-Rooms: Age: Rec-Rooms: Kitchen: Rear-Acc.:

Tot-Area: En-Suite: : :

SM-1 2 Modern 1 Small No

<800 No : :

Price £75,000

Indices


k-Decision Tree

All Cases

SM-1 BR-3BB-1SM-2

1 B-Rm 4 B-Rm3 B-Rm2 B-Rm

Modern Modern

4 WF 3 LR

• Tree can be built automatically (see later).

• What if no. of bedrooms is less important (predictive) than age of house?


Case Adaptation

• Breathalyser – if actual consumption is 2 more than in

retrieved case add 0.5 to blood alcohol count.

• Property Valuation– for extra bedroom add x% to price

• More complex adaptation may be needed where solutions are plans or designs, rather than single values.


Retrieval revisited

• Objective: to find the case most applicable to the current one.

• Applicable ?– If there is no adaptation, find case whose

solution we are most confident of reusing• i.e. whose differences don’t invalidate the solution

– With adaptation, find case whose solution is easiest to adapt to current problem

• use an adaptation cost measure instead of similarity measure.


Advantages of CBR

• May work better than inductive and deductive methods for natural domains.

• Does not require extensive analysis of domain knowledge.

• Existing data and knowledge - case histories, repair logs - are leveraged.

• Shortcuts complex reasoning - may be quicker than rule-based or model-based.


Problems with CBR

• Lack of deep knowledge -– poor explanation– danger of misapplication of cases.

• Large case base can slow things down– (compute-store tradeoff)

• Knowledge engineering can still be arduous– designing and selecting features– similarity matching algorithms


Hybrid Systems

• Integrate two or more reasoning methods to get a cooperative effect.

• See Protos system– builds a model from cases with “teacher” help– better explanation and more convincing


References and Acknowledgements

• Padraig Cunningham provided much of the material on CBR.

• Luger and Stubblefield: Third Edition of “Artificial Intelligence” has a lot more than the previous edition.

Documents

Knowledge-based Systems