ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด

1. ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัดขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด อาจารย์ที่ปรึกษาวิทยานิพนธ์ รศ. ดร. ประภาส จงสถิตย์วัฒนา ประธานกรรมการ ศ. ดร. ชิดชนก เหลือสินทรัพย์ กรรมการ ผศ. ดร. บุญเสริม กิจศิริกุล ดร. ณชล ไชยรัตนะ เสนอโดย นายนัทที นิภานันท์เลขประจำตัว 403 02410 21

2. The Story so far • There is a task X • Process A is a better way to do task X than any previously known method • under some measurement • A can be improved • Finding method A1 which is better than A

3. Presentation Outline • What is the task X? • What is process A? • and B, C, etc. • Why A is better than B, C, etc.? • What point in A that can be improved • Boring stuffs (but essential) • work plan, objective, scopes, benefit

4. Introduction Target Machines HypothesisMachine ? ? ? ≡ • Mimic the target machine INPUT OUTPUT LearningMethod

5. Introduction InductiveInference Process of hypothesizing a general rule from example ... GrammaticalInference Inference of any structure that can recognize a language DFA Inference ... Inference of DFA

6. Application • Digital circuit design • synthesis of finite state controller from observed I/O signal

7. Related Works GrammaticalInference PDA TuringMachine DFA Heuristic Minimal Inference • TraxBar • EDSM • Blue-fringe Method A Search GA • Biermann • BIC • Aporntewan

8. Heuristic Method : characteristic • Fast, highly scalable • No constraint on the size of hypothesis • O(T3H)

9. Search Method : characteristic • Slower than state heuristic • Very strong constraint on the size of hypothesis • Better accuracy than heuristic when training set is sparse • Search space is exponential on the size of training set (on fixed target size) • O(HT)

10. GA Method : characteristic • Slow • Strong constraint on the size of hypothesis • Search space is constant on the size of training set (on fixed target size)

11. Method Choosing SizeConstrain? Blue-fringe LargeTraining set BIC GA

12. Heuristic Method • State merging algorithm • Construct a prefix tree acceptor from given examples • Merge a pair of states 0 C Positive Example 00 1 Negative Example 10 B 0 A 0 E D 1

13. Heuristic Method (cont.) D G • Each merge introduce new constrain • Early merge should be correct B E H A C F I D G B A C E F H I

14. Heuristic Method : variation • TraxBar algorithm • Merge by Breadth First Search order • EDSM algorithm • Merge by score of evident • Compute score on every pairs • Blue-fringe algorithm • Merge by score of evident • Compute score only in candidate pairs • Much faster than EDSM, with very little accuracy loss

15. Heuristic Method : Blue-fringe • Starting with red at root • Children of red is blue • Compute and merge only red-blue pair • blue can be promoted to red

16. Search Method • Based on Biermann’s algorithm • Create Loop Free DFA L = (Q’,Σ,Δ,δ’,λ’,q’0) • Find mapping function F(q’) of the states of L to the states q of hypothesis DFA M = (Q,Σ,Δ,δ,λ,q0) • another form of state merging • use exhaustive searching • Define Si as the index of the state in the target automaton which state q’i in the LFDFA maps to. F(q’i) = qSi

17. Search Method (cont.) • Search step (assume hypothesis of N states) • 1. Select variable Sj to be assigned from unassigned S • 2. Assign value from 0 .. N-1 to Sj, if no more value exists, undo last assignment. • 3. If current assignment conflict with the constraints, undo and go to step 2. Else go to step 1.

18. Search Method (cont.) • Training set pose constraint on S • incompatible state • Problem can be viewed as constraint satisfactory problem (CSP)

19. Search Method : BIC • By Oliveira and Silva • Specialized CSP solver • Conflict diagnosis • analyze of conflict • remember the conflict for future prunning • Non-chronological backtracking • backjump to the level of the cause of conflict

20. GA Method • Search along all less than or equal n-states Mealy machine • impose target size constraint • Evaluate according to consistency of training set • Larger training set does not expand the search space • but took (linearly) more time in evaluation

21. GA Method: Aporntewan’s Method • Encodeδ and λ in bit string • Single point crossover • Evaluate by counting different output bit ... Next State Output Next State Output Next State Output Next State Output 0-transition 1-transition 0-transition 1-transition State 0 State N HypothesisMachine HypothesisOUTPUT INPUTSequence Compare OUTPUTSequence OUTPUTSequence

22. Attack Point • Find a better way of evaluation • Better search guidance • Find new encoding • Reduce encoding redundancy • Find a way to reduce destructive effect of crossover • Short defining length encoding • New crossover operator

23. Attack Point : Evaluation 0/B • Evaluation by checking output can mislead the search process B A 0/A 1/A 1/B Target Machine 0/A B A 0/B 1/B 1/A Hypothesis Machine

24. Attack Point : Encoding 0 • Some machines are behavioral equivalence while they differ in encoding 0 B A 1 1 B C B C A B C 1 0 Machine A 0 C 0 C B C B A C A 1 1 B 1 0 Machine B

25. Attack Point : Crossover • Crossover that • Reduce disruption effect • Knowledge of linkage • Compact representation • Better understanding of underlying structure A A A A A A A

26. Work Plan • Study the works in the related fields • Set up a reference method • Develop a new method • Set up an experiment • compare new method with reference method • Validate and summarize the result from the experiment • Conclude the research • Write a thesis

27. Objective • To develop a better genetic algorithm method for the problem

28. Scope of the research • Compare the new method with reference genetic algorithm method • The new method must be better than the reference method • The solutions from the new method must be shown to be consistency

29. Benefit • Having a better genetic algorithm method for the problem