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Learning Regular Expressions from Representative Examples and Membership Queries [chapter]

Efim Kinber
2010 Lecture Notes in Computer Science  
Learning Model: Learning an (unambiguous) *-expression R (of loop depth at most 2) from: • one representative examplemembership queries Output: a strongly left-aligned unambiguous *-expression equivalent  ...  : key ideas • Learning algorithm: an example • Correctness and complexity • Possible modifications • Related work Regular Expressions to be Learnt: • Unionless (called *-expressions) • Loop depth at most  ...  Learning Algorithm: Main Ideas (continued) Similarly, having synthesized a*ba*ba*ba*b from aaabaaaaaabaabaaaab, test "aaabaaaaabaab in L?" ("yes") "aaabaaaaab in L?" ("yes") "aaab in L?"  ... 
doi:10.1007/978-3-642-15488-1_9 fatcat:d7vnyhaeffctpg26v2ixgaqdru

On Learning Nominal Automata with Binders

Yi Xiao, Emilio Tuosto
2019 Electronic Proceedings in Theoretical Computer Science  
This class of automata captures nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders.  ...  We propose a learning algorithm on nominal regular languages with binders. Our algorithm generalises Angluin's L* algorithm with respect to nominal regular languages with binders.  ...  We designed a learning algorithm for a class of languages over infinite alphabet; more precisely, we have considered nominal regular languages with binders [25, 27] .  ... 
doi:10.4204/eptcs.304.9 fatcat:3g22jydynjdxda6yrdms2gd7ty

Learning to Prove Safety over Parameterised Concurrent Systems (Full Version) [article]

Yu-Fang Chen, Chih-Duo Hong, Anthony W. Lin, Philipp Ruemmer
2017 arXiv   pre-print
We have tested L* algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols  ...  We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular.  ...  Firstly, membership queries (i.e. is a word w reachable from Init ?)  ... 
arXiv:1709.07139v2 fatcat:qk4zvwgbsbcvdbkietnyxul744

Learning to prove safety over parameterised concurrent systems

Yu-Fang Chen, Chih-Duo Hong, Anthony W. Lin, Philipp Rummer
2017 2017 Formal Methods in Computer Aided Design (FMCAD)  
We have tested L * algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols  ...  We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular.  ...  Firstly, membership queries (i.e. is a word w reachable from Init?)  ... 
doi:10.23919/fmcad.2017.8102244 dblp:conf/fmcad/ChenHLR17 fatcat:vvswunfutffj5hh6gtp2xgt2o4

Using Language Inference to Verify Omega-Regular Properties [chapter]

Abhay Vardhan, Koushik Sen, Mahesh Viswanathan, Gul Agha
2005 Lecture Notes in Computer Science  
We have implemented the technique in a tool called Lever and use it to analyze some examples.  ...  We extend the machine learning based approach to verifying general ω-regular properties that include both safety and liveness.  ...  We introduced the learning to verify approach in [17] , where we used RPNI [12] to learn the regular set from positive and negative examples without active queries.  ... 
doi:10.1007/978-3-540-31980-1_4 fatcat:yn74k7reiffp3co5zbvlexmbjq

LEVER: A Tool for Learning Based Verification [chapter]

Abhay Vardhan, Mahesh Viswanathan
2006 Lecture Notes in Computer Science  
Since regular sets are used as the underlying symbolic representation for sets of states, we need an algorithm for learning regular sets using membership and equivalence queries.  ...  We use a learning framework that allows the learner to make two kinds of queries: a membership query asking if a particular element is a member of the target set and an equivalence query asking if a proposed  ... 
doi:10.1007/11817963_43 fatcat:4cfwwhjmijecxkd53n53mgycq4

Learning Regular Languages over Large Alphabets [chapter]

Oded Maler, Irini-Eleftheria Mens
2014 Lecture Notes in Computer Science  
We then extend Angluin's L * algorithm for learning regular languages from examples for such automata.  ...  An automaton learned by our procedure using 418 membership queries, 27 equivalence queries with a table of size 46 × 11.  ...  Learning Concrete Automata We briefly survey Angluin's L * algorithm [Ang87] for learning regular sets from membership queries and counter-examples, with slightly modified definitions to accommodate  ... 
doi:10.1007/978-3-642-54862-8_41 fatcat:g4gvqfamcjbgngv2ozjdtdx2em

An Efficient Learning Algorithm for Regular Pattern Languages Using One Positive Example and a Linear Number of Membership Queries

Satoshi MATSUMOTO, Tomoyuki UCHIDA, Takayoshi SHOUDAI, Yusuke SUZUKI, Tetsuhiro MIYAHARA
2020 IEICE transactions on information and systems  
The class of languages of regular patterns was known to be identifiable from one positive example using a polynomial number of membership queries, in the query learning model.  ...  example. key words: pattern language, membership query, query learning, computational learning theory  ...  Acknowledgments We would like to thank the anonymous reviewers for their valuable and helpful comments on our manuscript.  ... 
doi:10.1587/transinf.2019fcp0009 fatcat:gqzvvcrdtbfehnfcw7og6qatri

Active Automata Learning in Practice [chapter]

Falk Howar, Bernhard Steffen
2018 Lecture Notes in Computer Science  
We predicted four major topics to be addressed in the then near future: efficiency, expressivity of models, bridging the semantic gap between formal languages and analyzed components, and solutions to  ...  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.  ...  Kasprzik shows how residual finite-state tree automata can be inferred from membership queries and positive examples [95] .  ... 
doi:10.1007/978-3-319-96562-8_5 fatcat:jtpzftrzvzf3tdtbncntfc4s4q

Query learning of derived ω-tree languages in polynomial time [article]

Dana Angluin, Timos Antonopoulos, Dana Fisman
2019 arXiv   pre-print
Specifically, our algorithm uses membership and equivalence queries to learn classes of ω-tree languages derived from weak regular ω-word languages in polynomial time.  ...  A previous claim of an algorithm to learn regular ω-trees due to Jayasrirani, Begam and Thomas [2008] is unfortunately incorrect, as shown in Angluin [2016].  ...  For a learning algorithm, the examples tested by membership queries and the counterexamples returned by equivalence queries need to be finitely represented.  ... 
arXiv:1802.04739v2 fatcat:ynt3mozptfcyjbzdau7pz4hz5i

Actively Learning to Verify Safety for FIFO Automata [chapter]

Abhay Vardhan, Koushik Sen, Mahesh Viswanathan, Gul Agha
2004 Lecture Notes in Computer Science  
Specifically, we assume that we are given a model of the system and we provide a novel procedure which answers both membership and equivalence queries for a representation of the reachable states.  ...  We define a new encoding scheme for representing reachable states and their witness execution; this enables the learning algorithm to analyze a larger class of FIFO systems automatically than a naive encoding  ...  However, typically learning algorithms also require either negative examples of the concept being learned or the ability to make membership and equivalence queries.  ... 
doi:10.1007/978-3-540-30538-5_41 fatcat:3slmxwath5hodikxjpwpn367ry

Improving Symbolic Automata Learning with Concolic Execution [chapter]

Donato Clun, Phillip van Heerden, Antonio Filieri, Willem Visser
2020 Lecture Notes in Computer Science  
Upon these extensions we develop two learning algorithms that reduce significantly the number of queries required to converge to the correct hypothesis.  ...  Sound and complete active learning techniques have been developed for several classes of languages and the corresponding automaton representation, however there are outstanding challenges that are limiting  ...  The experiments in this section are based on regular expressions taken from the AutomatArk [8] benchmark suite.  ... 
doi:10.1007/978-3-030-45234-6_1 fatcat:ax5o2vuwffg3bkeghqiplp2kuu

The Learnability of Symbolic Automata [chapter]

George Argyros, Loris D'Antoni
2018 Lecture Notes in Computer Science  
Using this idea, MAT * is able to learn automata operating over alphabets theories in which predicates are efficiently learnable using membership and equivalence queries.  ...  First, we present MAT * , a novel L * -style algorithm for learning symbolic automata using membership and equivalence queries, which treats the predicates appearing on transitions as their own learnable  ...  Loris D'Antoni was supported by National Science Foundation Grants CCF-1637516, CCF-1704117 and a Google Research Award.  ... 
doi:10.1007/978-3-319-96145-3_23 fatcat:7xf5c27qrjdobdbiitig2dponu

Preface

H. Arimura, S. Jain
2005 Theoretical Computer Science  
The next paper by Denis, Gilleron and Letouzey considers learning from positive and unlabeled examples.  ...  The type of queries considered include membership, subset, superset and equivalence queries (along with their restricted forms).  ... 
doi:10.1016/j.tcs.2005.09.002 fatcat:jtfdzdc2o5hf3c65clujc66itm

Identifying unrecognizable regular languages by queries [chapter]

Claudio Ferretti, Giancarlo Mauri
1994 Lecture Notes in Computer Science  
We extend an algorithm de ned by Dana Angluin in 1987 for DFA's and using equivalence and membership queries.  ...  In trace languages the words are equivalence classes of strings, and we show how to extract, from a given class, a string that can drive the original learning algorithm.  ...  Introduction Considering the problem of learning formal languages from examples, and specifically regular languages, An87] gave an e cient algorithm to learn deterministic nite automata by membership  ... 
doi:10.1007/3-540-57868-4_72 fatcat:uu3n73sw6vespdguv7qo5odinu
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