Knowledge-based Systems

1
Knowledge-based Systems

• Alternatives to Rules

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.

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.

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.

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.

6
MBR Example
Predicted outputs
MULT-1
A3
ADD-1
(F12)
Actual F is 10
B3
MULT-2
C2
ADD-2
(G10)
D2
MULT-3
E3
Fig 6.14 of Luger and Stubblefield, Third Edition.
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-2s
  output is good).
• Discriminate between surviving hypotheses with
  further observations.
• E.g. check the actual output of MULT-1.

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.

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 wont
  work.

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.

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.

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

13
Components

• Representation
• Retrieval
• Matching engine retrieves cases similar to target
  case.
• Adaptation
• Remembering

14
Breathalyser

• Example cases

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

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.

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

17
Case indexing - Example
18
k-Decision Tree

• Tree can be built automatically (see later).
• What if no. of bedrooms is less important
  (predictive) than age of house?

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.

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 dont 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.

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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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

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

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.