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Grammatical Inference in Software Engineering: An Overview of the State of the Art [chapter]

Andrew Stevenson, James R. Cordy
2013 Lecture Notes in Computer Science  
examining the sentences of an unknown language.  ...  Grammatical inference -used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing -is the process of automatically inferring a grammar by  ...  These properties are exhibited by all regular languages, many context-free languages, and some context-sensitive languages. The algorithm is based on positive data and a membership oracle.  ... 
doi:10.1007/978-3-642-36089-3_12 fatcat:3qp3xzhhqjgwdhguesrjnvotra

A survey of grammatical inference in software engineering

Andrew Stevenson, James R. Cordy
2014 Science of Computer Programming  
examining the sentences of an unknown language.  ...  Grammatical inference -used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing -is the process of automatically inferring a grammar by  ...  These properties are exhibited by all regular languages, many context-free languages, and some context-sensitive languages. The algorithm is based on positive data and a membership oracle.  ... 
doi:10.1016/j.scico.2014.05.008 fatcat:xwasotc745ekbhaoq2n43vrufm

The Complexity of Learning Principles and Parameters Grammars [article]

Jacob Andreas
2012 arXiv   pre-print
We investigate models for learning the class of context-free and context-sensitive languages (CFLs and CSLs).  ...  Finally, we introduce a new family of subclasses, the principled parametric context-free grammars (and a corresponding family of principled parametric context-sensitive grammars), which roughly model the  ...  There exists a class of context-free languages with "natural" representations which are not learnable from equivalence queries in time polynomial in the size of the representation.  ... 
arXiv:1207.0052v3 fatcat:toydvm3agna7rajp5sk5d7fv2e

Inductive inference, DFAs, and computational complexity [chapter]

Leonard Pitt
1989 Lecture Notes in Computer Science  
The results discussed determine the extent to which DFAs can be feasibly inferred, and highlight a number of interesting approaches in computational learning theory.  ...  This paper surveys recent results concerning the inference of deterministic finite automata (DFAs).  ...  Acknowledgements I thank Dana Angluin and Manfred Warmuth for helpful and enjoyable discussions during the preparation of this manuscript.  ... 
doi:10.1007/3-540-51734-0_50 fatcat:cat5jh2pzvd6xbnbdodlh6t7bu

Ten Open Problems in Grammatical Inference [chapter]

Colin de la Higuera
2006 Lecture Notes in Computer Science  
They cover the areas of polynomial learning models, learning from ordered alphabets, learning deterministic Pomdps, learning negotiation processes, learning from context-free background knowledge.  ...  We propose 10 different open problems in the field of grammatical inference. In all cases, problems are theoretically oriented but correspond to practical questions.  ...  Acknowledgements Thanks to Jose Oncina for different discussions that led to several definitions and problems from sections 4 and 6.  ... 
doi:10.1007/11872436_4 fatcat:5snm4lpumbhw5e7ffh5vwxzhum

General Perspective on Distributionally Learnable Classes

Ryo Yoshinaka
2015 Proceedings of the 14th Meeting on the Mathematics of Language (MoL 2015)  
Those techniques have been applied to many formalisms richer than context-free grammars like multiple context-free grammars, simple contextfree tree grammars and others.  ...  Several algorithms have been proposed to learn different subclasses of context-free grammars based on the idea generically called distributional learning.  ...  Acknowledgments The view presented in this paper has been sharpened through the interactions and discussions with several researchers with whom I worked on the distributional learning of generalized CFGs  ... 
doi:10.3115/v1/w15-2308 dblp:conf/mol/Yoshinaka15 fatcat:q2ziqqernvepnnnat6or2kzhgy

Distributional learning of parallel multiple context-free grammars

Alexander Clark, Ryo Yoshinaka
2013 Machine Learning  
Our learning algorithm uses a nonprobabilistic learning paradigm which allows for membership queries as well as positive samples; it runs in polynomial time.  ...  Here we extend a family of distributional learning algorithms for context-free grammars to the class of Parallel Multiple Context-Free Grammars (PMCFGs).  ...  We say that a learning algorithm identifies a class G of grammars in the limit from positive data and membership queries if and only if it identifies all G ∈ G.  ... 
doi:10.1007/s10994-013-5403-2 fatcat:l6tkr4wgv5b2laj7pmo5v2wxje

Polynomial Time Learning of Some Multiple Context-Free Languages with a Minimally Adequate Teacher [chapter]

Ryo Yoshinaka, Alexander Clark
2012 Lecture Notes in Computer Science  
We present an algorithm for the inference of some Multiple Context-Free Grammars from Membership and Equivalence Queries, using the Minimally Adequate Teacher model of Angluin.  ...  We define the natural extension of the syntactic congruence to tuples of strings, and demonstrate we can efficiently learn the class of Multiple Context-Free Grammars where the non-terminals correspond  ...  They showed a polynomial identification in the limit result from positive data alone for a class of languages called the substitutable context free languages.  ... 
doi:10.1007/978-3-642-32024-8_13 fatcat:ziasohqdrffjhnl6ypx5svptf4

Distributional Learning of Some Context-Free Languages with a Minimally Adequate Teacher [chapter]

Alexander Clark
2010 Lecture Notes in Computer Science  
Clark and Eyraud (2007) showed that some context free grammars can be identified in the limit from positive data alone by identifying the congruence classes of the language.  ...  Angluin showed that the class of regular languages could be learned from a Minimally Adequate Teacher (mat) providing membership and equivalence queries.  ...  Acknowledgments I am very grateful to two anonymous reviewers for their helpful suggestions, and to Ryo Yoshinaka for some technical suggestions and corrections.  ... 
doi:10.1007/978-3-642-15488-1_4 fatcat:rjydzjrgw5ajdnt2qtuxppji6i

Learning Query Inseparable εℒℋ Ontologies

Ana Ozaki, Cosimo Persia, Andrea Mazzullo
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Given a fixed data instance A* and a query language 𝒬, we are interested in computing an ontology ℋ that entails the same queries as a target ontology 𝒯 on A*, that is, ℋ and 𝒯 are inseparable w.r.t  ...  Finally, we consider the PAC learning model and a setting where the algorithms learn from a batch of classified data, limiting interactions with the oracles.  ...  Acknowledgments This research has been supported by the Free University of Bozen-Bolzano through the projects PACO and MLEARN.  ... 
doi:10.1609/aaai.v34i03.5688 fatcat:ip2ejbczsvcrvl5pdbz4pj4mce

Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Ryo Yoshinaka
2011 Theoretical Computer Science  
Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.  ...  Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive  ...  finite state transducers and synchronous context-free languages.  ... 
doi:10.1016/j.tcs.2010.12.058 fatcat:ty2nnmdzmfc2nbzlto7vacu4ly

Editors' Introduction [chapter]

Sanjay Jain, Rémi Munos, Frank Stephan, Thomas Zeugmann
2013 Lecture Notes in Computer Science  
In his invited talk Towards General Algorithms for Grammatical Inference, Alexander Clark deals with the learning of context-free languages and multiple context-free languages.  ...  using a polynomial amount of data and processing time, provided that the distributions of the samples are restricted to be generated by one of a large family of related probabilistic deterministic finite  ...  In his invited talk Towards General Algorithms for Grammatical Inference, Alexander Clark deals with the learning of context-free languages and multiple context-free languages.  ... 
doi:10.1007/978-3-642-40935-6_1 fatcat:pchrsvhjezfbvh6dfplqhxhgcy

Threshold Treewidth and Hypertree Width

Robert Ganian, Andre Schidler, Manuel Sorge, Stefan Szeider
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
When either of these parameters is bounded by a constant, then CSP becomes solvable in polynomial time.  ...  However, here the order of the polynomial in the running time depends on the width, and this is known to be unavoidable; therefore, the problem is not fixed-parameter tractable parameterized by either  ...  Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
doi:10.24963/ijcai.2020/259 dblp:conf/ijcai/PersiaO20 fatcat:it4bzfmharbatgobyp4ybux4zu

On the Learnability of Possibilistic Theories [article]

Cosimo Persia, Ana Ozaki
2020 arXiv   pre-print
We consider cases in which only membership, only equivalence, and both kinds of queries can be posed by the learner.  ...  In particular, it follows from our results that the possibilistic extension of propositional Horn theories is exactly learnable in polynomial time.  ...  Acknowledgements We are supported by the University of Bergen. We would like to thank Andrea Mazzullo for joining initial discussions.  ... 
arXiv:2005.03157v1 fatcat:dy4oanbmyrgprbk7fcax5izhjy

Learning Query Inseparable ELH Ontologies [article]

Ana Ozaki, Cosimo Persia, Andrea Mazzullo
2020 arXiv   pre-print
Given a fixed data instance A* and a query language Q, we are interested in computing an ontology H that entails the same queries as a target ontology T on A*, that is, H and T are inseparable w.r.t.  ...  Finally, we consider the PAC learning model and a setting where the algorithms learn from a batch of classified data, limiting interactions with the oracles.  ...  Acknowledgments This research has been supported by the Free University of Bozen-Bolzano through the projects PACO and MLEARN.  ... 
arXiv:1911.07229v5 fatcat:fetip4iv5bebldseanrn6kq7om
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