Identification in the Limit of Systematic-Noisy Languages

2006 ◽  
pp. 19-31 ◽  
Author(s):  
Frédéric Tantini ◽  
Colin de la Higuera ◽  
Jean-Christophe Janodet
Keyword(s):  
Identification In The Limit

scholarly journals A Survey of State Merging Strategies for DFA Identification in the Limit

Triangle ◽  
2018 ◽  
pp. 121
Author(s):  
Cristina Tîrnăucă
Keyword(s):  
Active Learning ◽  
Grammatical Inference ◽  
Passive Learning ◽  
Characteristic Sets ◽  
Np Complete ◽  
Identification In The Limit ◽  
Important State ◽  
Comprehensive Study ◽  
Self Adaptive

Identication of deterministic nite automata (DFAs) has an extensive history, both in passive learning and in active learning. Intractability results by Gold [5] and Angluin [1] show that nding the smallest automaton consistent with a set of accepted and rejected strings is NP-complete. Nevertheless, a lot of work has been done on learning DFAs from examples within specic heuristics, starting with Trakhtenbrot and Barzdin's algorithm [15], rediscovered and applied to the discipline of grammatical inference by Gold [5]. Many other algorithms have been developed, the convergence of most of which is based on characteristic sets: RPNI (Regular Positive and Negative Inference) by J. Oncina and P. García [11, 12], Traxbar by K. Lang [8], EDSM (Evidence Driven State Merging), Windowed EDSM and Blue- Fringe EDSM by K. Lang, B. Pearlmutter and R. Price [9], SAGE (Self-Adaptive Greedy Estimate) by H. Juillé [7], etc. This paper provides a comprehensive study of the most important state merging strategies developed so far.

scholarly journals Polynomial Identification of $$\omega $$-Automata

2020 ◽  
pp. 325-343
Author(s):  
Dana Angluin ◽  
Dana Fisman ◽  
Yaara Shoval
Keyword(s):  
Polynomial Time ◽  
Negative Side ◽  
Positive Side ◽  
Language Classes ◽  
Characteristic Sample ◽  
Identification In The Limit ◽  
The Right

Abstract We study identification in the limit using polynomial time and data for models of $$\omega $$-automata. On the negative side we show that non-deterministic $$\omega $$-automata (of types Büchi, coBüchi, Parity or Muller) can not be polynomially learned in the limit. On the positive side we show that the $$\omega $$-language classes $$\mathbb {IB}$$, $$\mathbb {IC}$$, $$\mathbb {IP}$$, and $$\mathbb {IM}$$ that are defined by deterministic Büchi, coBüchi, parity, and Muller acceptors that are isomorphic to their right-congruence automata (that is, the right congruences of languages in these classes are fully informative) are identifiable in the limit using polynomial time and data. We further show that for these classes a characteristic sample can be constructed in polynomial time.

FAMILIES OF NONCOUNTING LANGUAGES AND THEIR LEARNABILITY FROM POSITIVE DATA

1996 ◽  
Vol 07 (04) ◽  
pp. 309-327 ◽  
Author(s):  
SATOSHI KOBAYASHI ◽  
TAKASHI YOKOMORI
Keyword(s):  
Polynomial Time ◽  
Close Relation ◽  
Boolean Operations ◽  
Positive Data ◽  
Language Classes ◽  
Identification In The Limit ◽  
The Relationship

This paper introduces some subclasses of noncounting languages and presents some results on the learnability of the classes from positive data. We first establish several relationships among the language classes introduced and the class of reversible languages. Especially, we introduce the notion of local parsability, and define a class (k, l)-CLTS, which is a subclass of the class of concatenations of strictly locally testable languages. We show its close relation to the class of reversible languages. We then study on the relationship between the closure of the Boolean operations and the learnability in the limit from positive data only. Further, we explore the learnability question of some subclasses of noncounting languages in the model of identification in the limit from positive data. In particular, we show that, for each k, l≥1, (k, l)-CLTS is identifiable in the limit from positive data using reversible automata with the conjectures updated in polynomial time. Some possible applications of the result are also briefly discussed.

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