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Learning Probabilistic Residual Finite State Automata [chapter]

Yann Esposito, Aurélien Lemay, François Denis, Pierre Dupont
2002 Lecture Notes in Computer Science  
We introduce a new class of probabilistic automata: Probabilistic Residual Finite State Automata.  ...  We prove that there are more languages generated by PRFA than by Probabilistic Deterministic Finite Automata (PDFA).  ...  Probabilistic residual finite state automata (PRFA) We introduce in this section the class of probabilistic residual finite state automata (PRFA).  ... 
doi:10.1007/3-540-45790-9_7 fatcat:go6ap7enwjhnflnwve4ayalj74

Residuality and Learning for Nondeterministic Nominal Automata [article]

Joshua Moerman, Matteo Sammartino
2022 arXiv   pre-print
This property enables active learning algorithms, and makes up for the fact that residuality -- a semantic property -- is undecidable for nominal automata.  ...  To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata.  ...  We thank Borja Balle for references on residual probabilistic languages, and Henning Urbat for discussions on nominal lattice theory.  ... 
arXiv:1910.11666v7 fatcat:w2qd6tv4o5hypbhu62urhlluwu

Residuality and Learning for Nondeterministic Nominal Automata

Joshua Moerman, Matteo Sammartino
2022 Logical methods in computer science : LMCS 18(1)  
This property enables active learning algorithms, and makes up for the fact that residuality -a semantic property -is undecidable for nominal automata.  ...  To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata.  ...  We thank Borja Balle for references on residual probabilistic languages, and Henning Urbat for discussions on nominal lattice theory.  ... 
doi:10.18154/rwth-2022-01916 fatcat:lyzgpncm7jcr5ijewbmsmknpey

PAutomaC: a probabilistic automata and hidden Markov models learning competition

Sicco Verwer, Rémi Eyraud, Colin de la Higuera
2013 Machine Learning  
The Probabilistic Automata learning Competition (PAutomaC), run in 2012, was the first grammatical inference challenge that allowed the comparison between these methods and algorithms.  ...  grammars and finite state machines and the relevant literature.  ...  residual finite state automata (Prfa) [31] , and multiplicity automata (Ma) [9, 8] (or weighted automata [54] ).  ... 
doi:10.1007/s10994-013-5409-9 fatcat:ey3ghvxqxzbevmpzxmv5tytvay

Active Automata Learning in Practice [chapter]

Falk Howar, Bernhard Steffen
2018 Lecture Notes in Computer Science  
Five years ago (in 2011) we have surveyed the then current state of active automata learning research and applications of active automata learning in practice.  ...  Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer.  ...  finite state automata.  ... 
doi:10.1007/978-3-319-96562-8_5 fatcat:jtpzftrzvzf3tdtbncntfc4s4q

Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms

P. Dupont, F. Denis, Y. Esposito
2005 Pattern Recognition  
On the other hand, HMMs with final probabilities and probabilistic automata generate distributions over strings of finite length.  ...  A semi-probabilistic automaton 2 (semi-PA) is a 5-tuple , Q, , , where is a finite alphabet, Q is a finite set of states, : Q × × Q → [0, 1] is a mapping defining the transition probability function, :  ...  Esposito et al. study the identification of probabilistic residual finite state automata (PRFA). The PRFA class includes properly the PDFA class and is strictly included in the PNFA class [45] .  ... 
doi:10.1016/j.patcog.2004.03.020 fatcat:uytkvkyfpfbj3lhibry6cfwue4

Some Classes of Regular Languages Identifiable in the Limit from Positive Data [chapter]

François Denis, Aurélien Lemay, Alain Terlutte
2002 Lecture Notes in Computer Science  
Probabilistic Residual Finite State Automata p. 77 Fragmentation: Enhancing Identifiability p. 92 On Limit Points for Some Variants of Rigid Lambek Grammars p. 106 Generalized Stochastic Tree  ...  Automata for Multi-relational Data Mining p. 120 On Sufficient Conditions to Identify Classes of Grammars from Polynomial Time and Data p. 134 Stochastic Grammatical Inference with Multinomial Tests  ... 
doi:10.1007/3-540-45790-9_6 fatcat:nmlknwqoyfbybhb6rpomqrn7qy

Learning Classes of Probabilistic Automata [chapter]

François Denis, Yann Esposito
2004 Lecture Notes in Computer Science  
Probabilistic finite automata (PFA) model stochastic languages, i.e. probability distributions over strings. Inferring PFA from stochastic data is an open field of research.  ...  Finally, we propose a learning algorithm for a subclass of PFA, called PRFA.  ...  Probabilistic Residual Finite Automata Definition 2 (Probabilistic Residual Finite Automaton).  ... 
doi:10.1007/978-3-540-27819-1_9 fatcat:e5cxnbd3drb73m55w3jflpttbu

Rational stochastic languages [article]

François Denis
2006 arXiv   pre-print
We define the notion of residual of a stochastic language and we use it to investigate properties of several subclasses of rational stochastic languages.  ...  Lastly, we study the representation of rational stochastic languages by means of multiplicity automata.  ...  Residual Automata (PRA), i.e. probabilistic automata whose structure is a residual finite state automaton (RFSA) [CO94, CO99, dlHT00, ELDD02, DE04] .  ... 
arXiv:cs/0602093v1 fatcat:4gz4cyxfqber3f5puh5gpljwx4

How to measure the topological quality of protein grammars? [article]

Witold Dyrka, François Coste, Olgierd Unold, Łukasz Culer, Agnieszka Kaczmarek
2017 arXiv   pre-print
Context-free and context-sensitive formal grammars are often regarded as more appropriate to model proteins than regular level models such as finite state automata and Hidden Markov Models.  ...  In theory, the claim is well founded in the fact that many biologically relevant interactions between residues of protein sequences have a character of nested or crossed dependencies.  ...  Context-free (CF) and context-sensitive (CS) formal grammars are often regarded as more appropriate to model proteins than regular level models such as finite state automata and Hidden Markov Models (HMM  ... 
arXiv:1611.10078v2 fatcat:eunzyetdwnaqdndmzn5ufnw7ke

Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference [article]

Amaury Habrard
2008 arXiv   pre-print
The estimate of P stands in some class of probabilistic models such as probabilistic automata (PA). In this paper, we focus on probabilistic models based on multiplicity automata (MA).  ...  In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language.  ...  A new state will be added to the automaton if the residual language corresponding to (ux) −1 p s cannot be approximated as a linear combination of the residual languages corresponding the states already  ... 
arXiv:cs/0607085v2 fatcat:2x3xe6svgfdevg67o2m6mwf4pi

Learning Probability Distributions Generated by Finite-State Machines [chapter]

Jorge Castro, Ricard Gavaldà
2016 Topics in Grammatical Inference  
The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata.  ...  to account for the error introduced by a finite sample.  ...  Probabilistic Automata A probabilistic finite automaton (pfa) of size n is a tuple Q, Σ , τ, α 0 , α ∞ where Q is a set of n states, Σ is a finite alphabet, τ : Q × Σ × Q → [0, 1] is the transition probability  ... 
doi:10.1007/978-3-662-48395-4_5 fatcat:u4cepbpghjcv7ct6zoqrgir2cy

Compositional Stochastic Model Checking Probabilistic Automata via Assume-guarantee Reasoning

Yang Liu, Rui Li
2020 International Journal of Networked and Distributed Computing (IJNDC)  
is a popular active learning algorithm (since they can ask queries actively) for Residual Finite-State Automata (RFSA) [35, 36] .  ...  Generally, the RFSA may generate extra nondeterministic choices in the product PA [37] and it is a subclass of Nondeterministic Finite-state Automata (NFA).  ... 
doi:10.2991/ijndc.k.190918.001 fatcat:55ee6cvpp5btfdox3qg7qezyci

Using Pseudo-stochastic Rational Languages in Probabilistic Grammatical Inference [chapter]

Amaury Habrard, François Denis, Yann Esposito
2006 Lecture Notes in Computer Science  
The estimate of P stands in some class of probabilistic models such as probabilistic automata (PA). In this paper, we focus on probabilistic models based on multiplicity automata (MA).  ...  In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language.  ...  A new state will be added to the automaton if the residual language corresponding to (ux) −1 p s cannot be approximated as a linear combination of the residual languages corresponding the states already  ... 
doi:10.1007/11872436_10 fatcat:zhpwf32bpngahkxkwcqblwg6qq

Automated Learning of Probabilistic Assumptions for Compositional Reasoning [chapter]

Lu Feng, Marta Kwiatkowska, David Parker
2011 Lecture Notes in Computer Science  
We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems.  ...  We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems.  ...  Whereas L* learns a minimal DFA for a regular language, the algorithm NL* [7] learns a minimal residual finite-state automaton (RFSA). RFSAs are a subclass of nondeterministic finite automata.  ... 
doi:10.1007/978-3-642-19811-3_2 fatcat:ghpdevjwxndgrhjo35deeqxgeq
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