Introduction Model Learning
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Model learning black box
- Passive: Process mining

- Active: Automata learning
	- Teacher and learner
	- Learner can ask
		- membership queries: yes/no from teacher
		- equivalence queries: yes/no+counterexample from teacher
	- Can be used for fingerprinting

Active Automata Learning
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- Mealy machine
- Nerode:
	two words are equivalent if for all continuations they map the same output
Observation table
Columns: suffix closed
Rows: prefix closed


HYPOTHESIS
- Two parts:
	- top part: states
	- bottom part: extensions
- Closedness:
	Bottom part structure should also be present in upper part otherwise move
	up.
- Completeness:
	Drawing should be input complete, otherwise add rows with extensions
	This means that a top part prefix plus suffix should be available in the
	bottom part

EQUIVALENCE QUERY
Yes: successfully found model
No: Counterexample:
	word w should give b

HANDLE CE
- extract suffix that disproves nerode
	- there is a suffix that ends you up in the same state, the later part is
	  the culprit.
	- add prefix above line, add suffix in column
- add to column(note that columns should be suffix closed)
- fill in table
- refine hypothesis