+Introduction Model Learning
+===============================================================================
+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
+===============================================================================
+- 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
+
+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 to column(note that columns should be suffix closed)
+- fill in table
+- refine hypothesis