Learning Qualitative Models Ivan Bratko, Dorian Suc

Presented by Cem Dilmegani

FEEL FREE TO ASK QUESTIONS DURING PRESENTATION

Summary● Understand QUIN algorithm● Explore the Crane Example● Analyze Learning Models expressed as QDEs

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

Modeling

●Modeling is complex●Modeling requires creativity●Solution: Use machine learning algorithms for modeling

Learning

examples hypothesis

Hypothesis

examples

learning

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)

●Minimal cost QCF is sought●Cost= M+(inconsistencies or ambiguities between dataset and QCF)

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.

●Similar to QSI

Crane Simulation

QUIN Approach

●Looks counterintuitive?

●Yes, but it outperforms straightforward transformations of quantitative data to quantitative model, like regression

Identification of Operator's Skill

● 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

● 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

● 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● Algorithm:

Starts like GENMODEL Constructs new variables if needed

● Limitations: Biased towards the most specific model

Negative Examples

● 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)

● 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)

● 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

●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 PressWikipedia

Any Questions?

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