Machine Discovery in the Presence of Incomplete or Ambiguous Data

We define a logic for machine discovery, which we call learning with justified refutation, in the style of Mukouchi and Arikawa's learning with refutation. By comparison, our model is more tolerant of the learning agent's behaviour in two particular cases, which we call the cases of incomplete and ambiguous data, respectively. Consequently our formalism correctly learns or refutes a wider spectrum of language classes than its forerunner.