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ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด

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ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด - PowerPoint PPT Presentation


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ขั้นตอนวิธีเชิงพันธุกรรมสำหรับการอนุมานเครื่องจักรสถานะจำกัด. อาจารย์ที่ปรึกษาวิทยานิพนธ์ รศ. ดร. ประภาส จงสถิตย์วัฒนา ประธานกรรมการ ศ. ดร. ชิดชนก เหลือสินทรัพย์ กรรมการ ผศ. ดร. บุญเสริม กิจศิริกุล ดร. ณชล ไชยรัตนะ เสนอโดย นายนัทที นิภานันท์ เลขประจำตัว 403 02410 21.

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slide1

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

the story so far
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
presentation outline
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
introduction
Introduction

Target Machines

HypothesisMachine

? ? ?

  • Mimic the target machine

INPUT

OUTPUT

LearningMethod

introduction1
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

application
Application
  • Digital circuit design
    • synthesis of finite state controller from observed I/O signal
related works
Related Works

GrammaticalInference

PDA

TuringMachine

DFA

Heuristic

Minimal Inference

  • TraxBar
  • EDSM
  • Blue-fringe

Method A

Search

GA

  • Biermann
  • BIC
  • Aporntewan
heuristic method characteristic
Heuristic Method : characteristic
  • Fast, highly scalable
  • No constraint on the size of hypothesis
  • O(T3H)
search method characteristic
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)
ga method characteristic
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)
method choosing
Method Choosing

SizeConstrain?

Blue-fringe

LargeTraining set

BIC

GA

heuristic method
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

heuristic method cont
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

heuristic method variation
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
heuristic method blue fringe
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
search method
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
search method cont
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.
search method cont1
Search Method (cont.)
  • Training set pose constraint on S
    • incompatible state
  • Problem can be viewed as constraint satisfactory problem (CSP)
search method bic
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
ga method
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
ga method aporntewan s method
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

attack point
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
attack point evaluation
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

attack point encoding
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

attack point crossover
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

work plan
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
objective
Objective
  • To develop a better genetic algorithm method for the problem
scope of the research
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
benefit
Benefit
  • Having a better genetic algorithm method for the problem
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