Polynomial time learnability of simple deterministic languages from MAT and a representative sample
We propose a learning algorithm for simple deterministic languages from queries and a priori knowledge. To the learner, a special finite subset of the target language, called a representative sample, is provided at the beginning and two types of queries, equivalence queries and membership queries, are available. This learning algorithm constructs nonterminals of a hypothesis grammar based on Ishizaka(1990)'s idea. In Ishizaka(1990)'s algorithm, the learner makes rules as many as possible from positive counterexamples, and diagnoses wrong rules from negative counterexamples. In contrast, our algorithm guesses a simple deterministic grammar and diagnoses them using positive and negative counterexamples based on Angluin(1987)'s algorithm.
simple deterministic languages representative sample learning via queries mat learning