march 1999dip hi kbs1 knowledge-based systems alternatives to rules
TRANSCRIPT
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March 1999 Dip HI KBS 1
Knowledge-based Systems
Alternatives to Rules
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March 1999 Dip HI KBS 2
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.
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March 1999 Dip HI KBS 3
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.
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March 1999 Dip HI KBS 4
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.
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March 1999 Dip HI KBS 5
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.
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March 1999 Dip HI KBS 6
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.
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March 1999 Dip HI KBS 7
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.
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March 1999 Dip HI KBS 8
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.
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March 1999 Dip HI KBS 9
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.
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March 1999 Dip HI KBS 10
Advantages of MBR
• More robust and flexible reasoning
• Can provide causal explanations. May serve a tutorial role.
• Knowledge may be transferable to related tasks.
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March 1999 Dip HI KBS 11
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.
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March 1999 Dip HI KBS 12
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
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March 1999 Dip HI KBS 13
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
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March 1999 Dip HI KBS 14
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?
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March 1999 Dip HI KBS 15
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.
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March 1999 Dip HI KBS 16
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
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March 1999 Dip HI KBS 17
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
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March 1999 Dip HI KBS 18
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?
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March 1999 Dip HI KBS 19
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.
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March 1999 Dip HI KBS 20
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.
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March 1999 Dip HI KBS 21
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.
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March 1999 Dip HI KBS 22
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
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March 1999 Dip HI KBS 23
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
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March 1999 Dip HI KBS 24
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.