Learning Qualitative Models
Ivan Bratko, Dorian Suc
Presented by Cem Dilmegani
FEEL FREE TO ASK QUESTIONS DURING
PRESENTATION
Summary
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Understand QUIN algorithm
●
Explore the Crane Example
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Analyze Learning Models expressed as QDEs
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
GENMODEL by Coiera

QSI by Say and Kuru

QOPH by Coghill et Al.

ILP Systems
Conclusion

Applications

Further Progress
Modeling
●
Modeling is complex
●
Modeling requires creativity
Solution: Use machine learning algorithms for
modeling
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Modeling
●
Modeling is complex
●
Modeling requires creativity
Solution: Use machine learning algorithms for
modeling
●
Learning
examples
learning
Hypothesis
examples
hypothesis
Decision Tree
Decision Tree Algorithm
QUIN (QUalitative INduction)
●
●
Looks for qualitative patterns in quantitative
data
Uses so-called qualitative trees
Qualitative tree
The splits define a partition of the attribute space
into areas with common qualitative behaviour of
the class variable
Qualitatively constrained functions (QCFs) in
leaves define qualitative constraints on the class
variable
Qualitatively constrained functions
(QCFs)
The qualitative
constraint given by the
sign only states that
when the i-th attribute
increases, the QCF will
also change in the
direction specified in M,
barring other changes.
Qualitative Tree Example
Explanation of Algorithm(Leaf Level)
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Minimal cost QCF is sought
Cost= M+(inconsistencies or ambiguities between
dataset and QCF)
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Consistency
A QCV (Qualitative Change Vector) is consistent with a
QCF if either a) class qualitative change is zero b) all
attributes QCF-predictions are zero or c) there exists an
attribute whose QCF prediction is equal to the class'
qualitative change
 Z=M+,-(X,Y)
● a) no change = (inc,dec)
● a) no change = (inc,inc)
● b) * = (no change, no change)
● c) inc = (inc, dec)
●
Ambiguity
A qualitative ambiguity appears a) when there exist both
positive and negative QCF-predictions b) whenever all
QCF-predictions are 0.
 Z=M+,-(X,Y)
● a) * = (inc,inc)
● b) * = (no change, no change)
●
Ambiguity-Inconsistency
Explanation of Algorithm
●
●
Start with QCF that minimizes cost in one attribute and
then use “error-cost” to refine the current QCF with
another attribute
Tree Level algorithm: QUIN chooses best split by
comparing the partitions of the examples it generates:
for every possible split, it splits the examples into 2
subsets (according to the split), finds the minimal cost
QCF in both subsets and selects the split which
minimizes the tree error cost. This goes on until, a
specified error bound is reached.
Qualitative Reverse Engineering
In the industry, there exists library of designs and
corresponding simulation models which are not
well documented
●
We may have to reverse engineer complex
simulations to understand how the simulation
functions.
●
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Similar to QSI
Crane Simulation
QUIN Approach
●
Looks counterintuitive?
Yes, but it outperforms
straightforward transformations
of quantitative data to
quantitative model, like
regression
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Identification of Operator's Skill
●
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Can't be learnt from operator verbally (Bratko
and Urbancic 1999)
Skill is manifested in operator's actions, QUIN is
better at explaining those skills than quantitative
models
Comparison of 2 operators
S (slow)
L (adventurous)
Explanation of S's Strategy
●
●
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At the beginning V increases as X increases
(load behind crane)
Later, V decreases as X increases (load
gradually moves ahead of crane)
V increases as the angle increases (crane
catches up with the load
GENMODEL by Coiera
●
QSI without hidden variables
●
Algorithm:

Construct all possible constraints using all observed
variables

Evaluate all constraints

Retain those constraints that are satisfied by all
states, discard all other

The retained constraints are your model
GENMODEL by Coiera
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Limitations:

Assumes that all variables are observed

Biased towards the most specific models
(overfitting)

Does not support operating regions
QSI by Say and Kuru
●
Explained last week
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Algorithm:
●

Starts like GENMODEL

Constructs new variables if needed
Limitations:

Biased towards the most specific model
Negative Examples
●
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Consider U-Tube Example

Conservation of water until the second tube bursts
or overflows

There can not be negative amounts of water in a
container
Evaporation?
Inductive Logic Programming (ILP)
●
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ILP is a machine learning approach which uses
techniques of logic programming.
From a database of facts which are divided into
positive and negative examples, an ILP system
tries to derive a logic program that proves all
the positive and none of the negative examples.
Inductive Logic Programming (ILP)
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Advantages:

No need to create a new program, uses established
framework

Hidden variables are introduced

Can learn models with multiple operating regions as
well
Applications
German car manufacturer simplified their wheel
suspension system with QUIN
●
Induction of patient-specific models from patients'
measured cardio vascular signals using
GENMODEL
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An ILP based learning system (QuMAS) learnt
the electrical system of the heart and is able to
explain many types of cardiac arrhythmias
●
Suggestions for Further Progress
Better methods for transforming numerical data
into qualitative data
●Deeper study of principles or heuristics
associated with the discovery of hidden variables
●More effective use of general ILP techniques.
●
Sources
Dorian Suc, Ivan Bratko “Qualitative Induction”
Ethem Alpaydin “Introduction to Machine
Learning” MIT Press
Wikipedia
Any Questions?
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