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View all- Angluin DEisenstat SFisman D(2015)Learning regular languages via alternating automataProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832581.2832710(3308-3314)Online publication date: 25-Jul-2015
Inference of residual finite-state tree automata from membership queries and finite positive data
Pages 476 - 477
Abstract
The area of Grammatical Inference centers on learning algorithms: Algorithms that infer a description (e.g., a grammar or an automaton) for an unknown formal language from given information in finitely many steps. Various conceivable learning settings have been outlined, and based on those a range of algorithms have been developed. One of the language classes studied most extensively with respect to its algorithmical learnability is the class of regular string languages.
Possible sources of information include membership queries (MQs) where a learner may query an oracle if a certain element is in the target language L, and equivalence queries (EQs) where a learner may ask if the current hypothesis is correct and is given a counterexample if this is not the case. Moreover, a learner can for example be presented with a positive sample, i.e., a finite subset of L.
References
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Angluin, D.: Learning regular sets from queries and counterexamples. Information and Computation 75(2), 87-106 (1987).
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Denis, F., Lemay, A., Terlutte, A.: Residual finite state automata. Fundamentae Informaticae 51, 339-368 (2002).
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Angluin, D.: A note on the number of queries needed to identify regular languages. Information and Control 51(1), 76-87 (1981).
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Sakakibara, Y.: Learning context-free grammars from structural data in polynomial time. Theoretical Computer Science 76(2-3), 223-242 (1990).
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Drewes, F., Högberg, J.: Learning a regular tree language from a teacher. In: Ésik, Z., Fülöp, Z. (eds.) DLT 2003. LNCS, vol. 2710, pp. 279-291. Springer, Heidelberg (2003).
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Besombes, J., Marion, J.-Y.: Learning tree languages from positive examples and membership queries. Theoretical Computer Science 382, 183-197 (2007).
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Denis, F., Lemay, A., Terlutte, A.: Learning regular languages using RFSA. In: Abe, N., Khardon, R., Zeugmann, T. (eds.) ALT 2001. LNCS (LNAI), vol. 2225, pp. 348-363. Springer, Heidelberg (2001).
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Bollig, B., Habermehl, P., Kern, C., Leucker, M.: Angluin-style learning of NFA. In: Online Proceedings of IJCAI, vol 21 (2009).
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Carme, J., Gilleron, R., Lemay, A., Terlutte, A., Tommasi, M.: Residual finite tree automata. In: Ésik, Z., Fülöp, Z. (eds.) DLT 2003. LNCS, vol. 2710, pp. 171-182. Springer, Heidelberg (2003).
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- Inference of residual finite-state tree automata from membership queries and finite positive data
Index terms have been assigned to the content through auto-classification.
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- EATCS: European Association for Theoretical Computer Science
- The University of Milano-Bicocca: The University of Milano-Bicocca
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Berlin, Heidelberg
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Published: 19 July 2011
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View all- Angluin DEisenstat SFisman D(2015)Learning regular languages via alternating automataProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832581.2832710(3308-3314)Online publication date: 25-Jul-2015