Learning Qualitative Models Ivan Bratko, Dorian Suc Presented by Cem Dilmegani

1. Learning Qualitative Models Ivan Bratko, Dorian Suc Presented by Cem Dilmegani FEEL FREE TO ASK QUESTIONS DURING PRESENTATION

2. 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

3. Modeling • Modeling is complex • Modeling requires creativity • Solution: Use machine learning algorithms for modeling

4. Modeling • Modeling is complex • Modeling requires creativity • Solution: Use machine learning algorithms for modeling

5. examples hypothesis Learning learning Hypothesis examples

6. Decision Tree

7. Decision Tree Algorithm

8. QUIN (QUalitative INduction) • Looks for qualitative patterns in quantitative data • Uses so-called qualitative trees

9. 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 Qualitative tree

10. 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.

11. Qualitative Tree Example

12. Explanation of Algorithm(Leaf Level) • Minimal cost QCF is sought • Cost= M+(inconsistencies or ambiguities between dataset and QCF)

13. 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)

14. 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)

15. Ambiguity-Inconsistency

16. 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.

17. 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 Qualitative Reverse Engineering

18. Crane Simulation

19. Looks counterintuitive? Yes, but it outperforms straightforward transformations of quantitative data to quantitative model, like regression QUIN Approach

20. 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

21. S (slow) L (adventurous) Comparison of 2 operators

22. 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

23. 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

24. GENMODEL by Coiera • Limitations: • Assumes that all variables are observed • Biased towards the most specific models (overfitting) • Does not support operating regions

25. QSI by Say and Kuru • Explained last week • Algorithm: • Starts like GENMODEL • Constructs new variables if needed • Limitations: • Biased towards the most specific model

26. 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?

27. 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.

28. 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

29. 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 Applications

30. 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. Suggestions for Further Progress

31. Dorian Suc, Ivan Bratko “Qualitative Induction” Ethem Alpaydin “Introduction to Machine Learning” MIT Press Wikipedia Sources

32. ??????? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? Any Questions?