Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Inductive inference from all positive and some negative data
Information Processing Letters
Language learning with some negative information
Journal of Computer and System Sciences
Machine Learning
Machine Learning
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Learning languages from positive data and negative counterexamples
Journal of Computer and System Sciences
Learning languages from positive data and a finite number of queries
Information and Computation
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We consider two variants of a model for learning languages in the limit from positive data and a limited number of short negative counterexamples (counterexamples are considered to be short if they are smaller that the largest element of input seen so far). Negative counterexamples to a conjecture are examples which belong to the conjectured language but do not belong to the input language. Within this framework, we explore how/when learners using n short (arbitrary) negative counterexamples can be simulated (or simulate) using least short counterexamples or just ‘no’ answers from a teacher. We also study how a limited number of short counterexamples fairs against unconstrained counterexamples. A surprising result is that just one short counterexample (if present) can sometimes be more useful than any bounded number of counterexamples of least size. Most of results exhibit salient examples of languages learnable or not learnable within corresponding variants of our models.