Zulu: An Interactive Learning Competition

Active language learning is an interesting task for which theoretical results are known and several applications exist. In order to better understand what the better strategies may be, a new competition called Zulu (http://labh-curien.univ-st-etienne.fr/zulu/) is launched: participants are invited to learn deterministic finite automata from membership queries. The goal is to obtain the best classification rate from a fixed number of queries.

[1]  Colin de la Higuera,et al.  Learning Languages from Bounded Resources: The Case of the DFA and the Balls of Strings , 2008, ICGI.

[2]  Dana Angluin,et al.  Queries revisited , 2001, Theoretical Computer Science.

[3]  DANA ANGLUIN,et al.  On the Complexity of Minimum Inference of Regular Sets , 1978, Inf. Control..

[4]  Osamu Watanabe,et al.  The query complexity of learning DFA , 1994, New Generation Computing.

[5]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[6]  Colin de la Higuera,et al.  Ten Open Problems in Grammatical Inference , 2006, ICGI.

[7]  David Carmel,et al.  Model-based learning of interaction strategies in multi-agent systems , 1998, J. Exp. Theor. Artif. Intell..

[8]  T. Ho,et al.  Data Complexity in Pattern Recognition , 2006 .

[9]  David Carmel,et al.  Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies , 1999, Autonomous Agents and Multi-Agent Systems.

[10]  G. Olsder Mathematical Systems Theory , 2011 .

[11]  Dana Angluin Negative results for equivalence queries , 1990, Mach. Learn..

[12]  Dana Angluin,et al.  A Note on the Number of Queries Needed to Identify Regular Languages , 1981, Inf. Control..

[13]  Sandro Spina,et al.  Grammatical Inference: Algorithms and Applications , 2004, Lecture Notes in Computer Science.

[14]  Jorge Castro,et al.  PACS, simple-PAC and query learning , 2000, Inf. Process. Lett..

[15]  Efim B. Kinber,et al.  On Learning Regular Expressions and Patterns Via Membership and Correction Queries , 2008, ICGI.

[16]  Kenneth Basye,et al.  Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning , 2004, Machine Learning.

[17]  Umesh V. Vazirani,et al.  An Introduction to Computational Learning Theory , 1994 .

[18]  Leonor Becerra-Bonache,et al.  Learning DFA from Correction and Equivalence Queries , 2006, ICGI.

[19]  Leonor Becerra-Bonache,et al.  Learning Balls of Strings from Edit Corrections , 2008, J. Mach. Learn. Res..

[20]  Colin de la Higuera,et al.  A bibliographical study of grammatical inference , 2005, Pattern Recognit..

[21]  Harald Raffelt,et al.  LearnLib: a library for automata learning and experimentation , 2005, FMICS '05.

[22]  Olivier Gascuel,et al.  Hidden Markov Models with Patterns to Learn Boolean Vector Sequences and Application to the Built-In Self-Test for Integrated Circuits , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Ronald L. Rivest,et al.  Inference of finite automata using homing sequences , 1989, STOC '89.

[24]  Colin de la Higuera,et al.  Learning probabilistic finite automata , 2010 .

[25]  Bengt Jonsson,et al.  On the Correspondence Between Conformance Testing and Regular Inference , 2005, FASE.

[26]  Colin de la Higuera,et al.  Data Complexity Issues in Grammatical Inference , 2006 .

[27]  Ahmed Saoudi,et al.  Learning local and recognizable Ω-languages and monadic logic programs , 1994, EuroCOLT.

[28]  Francesco Bergadano,et al.  Learning Behaviors of Automata from Multiplicity and Equivalence Queries , 1994, SIAM J. Comput..

[29]  Sampath Kannan,et al.  Oracles and Queries That Are Sufficient for Exact Learning , 1996, J. Comput. Syst. Sci..

[30]  Ricard Gavaldà,et al.  On the power of equivalence queries , 1994, EuroCOLT.

[31]  Colin de la Higuera,et al.  Learning Stochastic Finite Automata , 2004, ICGI.

[32]  Amaury Habrard,et al.  A Polynomial Algorithm for the Inference of Context Free Languages , 2008, ICGI.

[33]  Takashi Yokomori Learning two-tape automata from queries and counterexamples , 2005, Mathematical systems theory.

[34]  Osamu Watanabe,et al.  An optimal parallel algorithm for learning DFA , 1994, COLT '94.

[35]  Dana Angluin,et al.  Learning Regular Sets from Queries and Counterexamples , 1987, Inf. Comput..

[36]  Juan Miguel Vilar Query learning of subsequential transducers , 1996, ICGI.

[37]  Joachim Niehren,et al.  Interactive learning of node selecting tree transducer , 2006, Machine Learning.

[38]  Perdita Stevens,et al.  Modelling Recursive Calls with UML State Diagrams , 2003, FASE.

[39]  S. V. N. Vishwanathan,et al.  Learnability of Probabilistic Automata via Oracles , 2005, ALT.