Learning two-tape automata from queries and counterexamples
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
A PAC Learnability of Simple Deterministic Languages
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Consistent Polynominal Identification in the Limit
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Prediction-Preserving Reducibility with Membership Queries on Formal Languages
FCT '01 Proceedings of the 13th International Symposium on Fundamentals of Computation Theory
Polynomial time learning of simple deterministic languages via queries and a representative sample
Theoretical Computer Science
LARS: A learning algorithm for rewriting systems
Machine Learning
Learning deterministically recognizable tree series
Journal of Automata, Languages and Combinatorics
Language structure using fuzzy similarity
IEEE Transactions on Fuzzy Systems
Off-line identification of concurrent discrete event systems exhibiting cyclic behaviour
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
| Hi-index | 0.00 |
This paper is concerned with the problem of learning simple deterministic languages. The algorithm described in this paper is based on the theory of model inference given by Shapiro. In our setting, however, nonterminal membership queries, except for the start symbol, are not permitted. Extended equivalence queries are used instead. Nonterminals that are necessary for a correct grammar and their intended models are introduced automatically. We give an algorithm that, for any simple deterministic language L, outputs a grammar G in 2-standard form, such that L = L(G), using membership queries and extended equivalence queries. We also show that the algorithm runs in time polynomial in the length of the longest counterexample and the number of nonterminals in a minimal grammar for L.