Merge remote-tracking branch 'origin/master' into master

README.md

@@ -27,7 +27,7 @@ Whether you work with regular languages or you would like to learn models of
 
 | Automata Type   |      Supported Formalisms      |  Features |
 |----------|:-------------:|------:|
-| Deterministic     |  Deterministic Finite Automata<br />Mealy Machines<br />Moore Machines | Counterexample Processing<br />Seamless Caching<br />11 Eq. Oracles |
+| Deterministic     |  Deterministic Finite Automata<br />Mealy Machines<br />Moore Machines | Counterexample Processing<br />Seamless Caching<br />11 Equivalence Oracles |
 | Non-Deterministic |    Observable Non-Deterministic FSM <br /> Abstracted Non-Deterministic FSM|   Size Reduction Trough Abstraction<br />|
 | Stochastic        |  Markov Decision Processes<br />Stochastic Mealy Machines<br />Markov Chains |    Counterexample Processing<br />Row/Cell Compatability Metrics<br />Model Checking with PRISM<br />Alergia Passive Learning|
 
@@ -49,8 +49,8 @@ AALpy enables efficient learning by providing a **large set of equivalence oracl
 is mostly based on Angluin's [L* algorithm](https://people.eecs.berkeley.edu/~dawnsong/teaching/s10/papers/angluin87.pdf), for which AALpy supports a 
 selection of optimizations, including **efficient counterexample processing** and **caching**.
 
-AALpy also has an efficient implementation of the [Alergia](https://link.springer.com/article/10.1007/s10994-016-5565-9) algorithm, 
-suited for passive learning of Markov Chains and Markov Decision processes. With Alergia, one can also passively learn deterministic Moore machines.
+AALpy also has an efficient implementation of the [ALERGIA](https://link.springer.com/article/10.1007/s10994-016-5565-9) algorithm, 
+suited for passive learning of Markov Chains and Markov Decision processes. With ALERGIA, one can also passively learn deterministic Moore machines.
 
 ## Installation