Angluin, D. (1981). A note on the number of queries needed to identify regular languages. *Information and Control*, *51*, 76–87.

Angluin, D. (1987). Learning regular sets from queries and counterexamples. *Information and Computation*, *75*, 87–106.

Angluin, D. (1988). Queries and concept learning. *Machine Learning*, *2:4*, 319–342.

Castro, J., & Guijarro, D. (1998). Query, pacs and simple-pac learning. Technical Report LSI-98-2-R, Universitat Polytéctica de Catalunya, Spain.

Chomsky, N. (1956). Three models for the description of language. *PGIT*, *2:3*, 113–124.

Denis, F., D'Halluin, C., & Gilleron, R. (1996). Pac learning with simple examples. *STACS*'96—Proceedings of the 13^{th} Annual Symposium on the Theoretical Aspects of Computer Science (pp. 231–242).

Denis, F.,& Gilleron, R. (1997). Pac learning under helpful distributions. In *Proceedings of the Eighth International Workshop on Algorithmic Learning Theory (ALT'97)*, *Lecture Notes in Artificial Intelligence* 1316 (pp. 132–145), Sendai, Japan.

Dupont, P. (1996). Incremental regular inference. In L. Miclet, & C. Higuera, (Eds.), *Proceedings of the Third ICGI-96*, *Lecture Notes in Artificial Intelligence* 1147 (pp. 222–237), Montpellier,France, Springer.

Dupont, P. (1996). *Utilisation et apprentissage de modèles de language pour la reconnaissance de la parole continue*. PhD thesis, Ecole Normale Supérieure des Télécommunications, Paris, France.

Dupont, P., Miclet, L., & Vidal, E. (1994). What is the search space of the regular inference? In *Proceedings of the Second International Colloquium on Grammatical Inference (ICGI'94)* (pp. 25–37). Alicante, Spain.

Gold, E. (1978). Complexity of automaton identification from given data. *Information and Control*, *37:3*, 302–320.

Goldman, S., & Mathias, H. (1993). Teaching a smarter learner. In *Proceedings of theWorkshop on Computational Learning Theory (COLT'93)* (pp. 67–76). ACM Press.

Goldman, S., & Mathias, H (1996). Teaching a smarter learner. *Journal of Computer and System Sciences*, *52*, 255–267.

Colin de la Higuera (1996). Characteristic sets for polynomial grammatical inference. In L. Miclet, & C. Higuera, (Eds.), *Proceedings of the Third ICGI-96*, *Lecture Notes in Artificial Intelligence* 1147 (pp. 59–71). Montpellier, France, Springer.

Hopcroft, J., & Ullman, J. (1979). *Introduction to automata theory, languages, and computation*. Reading, MA: Addison Wesley.

Jackson, J., & Tomkins, A. (1992). A computational model of teaching. In *Proceedings of the Workshop on Computational Learning Theory (COLT'92)* (pp. 319–326). ACM Press.

Kearns, M., & Valiant, L. G. (1989). Cryptographic limitations on learning boolean formulae and finite automata. In *Proceedings of the 21st Annual ACM Symposium on Theory of Computing* (pp. 433–444). New York: ACM.

Lang, K. (1992). Random DFAs can be approximately learned from sparse uniform sample. In *Proceedings of the* 5*th ACM workshop on Computational Learning Theory* (pp. 45–52).

Li, M., & Vitányi, P. (1991). Learning simple concepts under simple distributions. *SIAM Journal of Computing*, *20:5*, 911–935.

Li, M., & Vitányi, P. (1997). *An introduction to Kolmogorov complexity and its applications*, (2nd ed.) New York: Springer Verlag.

Oncina, J., & Garcia, P. (1992). Inferring regular languages in polynomial update time. In N. Pérez et al. (eds.), *Pattern recognition and image analysis* (pp. 49–61). Singapore: World Scientific.

Pao, T., & Carr, J. (1978). A solution of the syntactic induction-inference problem for regular languages. *Computer Languages*, *3*, 53–64.

Parekh, R., & Honavar, V. (1993). Efficient learning of regular languages using teacher supplied positive examples and learner generated queries. *In Proceedings of the Fifth UNB Conference on AI* (pp. 195–203). Fredricton, Canada.

Parekh, R., & Honavar, V. (1997). Learning DFA from simple examples. In *Proceedings of the Eighth International Workshop on Algorithmic Learning Theory (ALT'97)*, *Lecture Notes in Artificial Intelligence* 1316 (pp. 116–131). Sendai, Japan, Springer. Also presented at the*Workshop on Grammar Inference, Automata Induction, and Language Acquisition* (ICML'97), Nashville, TN, July 12, 1997.

Parekh, R & Honavar, V. (1999). Simple DFA are polynomially probably exactly learnable from simple examples. In *Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)* (pp. 298–306). Bled, Slovenia.

Pitt, L. (1989). Inductive inference, DFAs and computational complexity. In *Analogical and Inductive Inference*, *Lecture Notes in Artificial Intelligence*, 397 (pp. 18–44). Springer-Verlag.

Pitt, L., & Warmuth, M. K. (1988). Reductions among prediction problems: on the difficulty of predicting automata. In *Proceedings of the 3rd IEEE Conference on Structure in Complexity Theory* (pp. 60–69).

Pitt, L., & Warmuth, M. K. (1989). The minimum consistency DFA problem cannot be approximated within any polynomial. In *Proceedings of the 21st ACM Symposium on the Theory of Computing* (pp. 421–432). ACM.

Rivest, R. L. & Schapire, R. E. (1993). Inference of finite automata using homing sequences. *Information and Computation*, *103:2*, 299–347.

Trakhtenbrot, B., & Barzdin, Ya. (1973). *Finite Automata: Behavior and Synthesis*. Amsterdam, North Holland.

Valiant, L. (1984). A theory of the learnable. *Communications of the ACM*, *27*, 1134–1142.