Communications of the ACM
On the complexity of inductive inference
Information and Control
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Learning regular sets from queries and counterexamples
Information and Computation
Foundations of deductive databases and logic programming
Trade-off among parameters affecting inductive inference
Information and Computation
Learning via queries with teams and anomilies
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Equivalence queries and approximate fingerprints
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Learning via queries to an oracle
COLT '89 Proceedings of the second annual workshop on Computational learning theory
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Algorithmic Program DeBugging
Automata on Infinite Objects and Church's Problem
Automata on Infinite Objects and Church's Problem
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Machine Learning
Machine Learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the structure of degrees of inferability
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Reductions for learning via queries
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning via queries and oracles
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
On the inductive inference of real valued functions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Aspects of complexity of conservative probabilistic learning
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Aspects of complexity of probabilistic learning under monotonicity constraints
Theoretical Computer Science - Algorithmic learning theory
Classification using information
Annals of Mathematics and Artificial Intelligence
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Learning power and language expressiveness
Theoretical Computer Science - Australasian computer science
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Learning via finitely many queries
Annals of Mathematics and Artificial Intelligence
Learning languages from positive data and a finite number of queries
Information and Computation
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Non-U-shaped vacillatory and team learning
Journal of Computer and System Sciences
Journal of Computer and System Sciences
Taming teams with mind changes
Journal of Computer and System Sciences
One-Shot Learners Using Negative Counterexamples and Nearest Positive Examples
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
One-shot learners using negative counterexamples and nearest positive examples
Theoretical Computer Science
Learning languages from positive data and a finite number of queries
Information and Computation
The complexity of learning SUBSEQ (A)
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Learning Via Queries With Teams And Anomalies
Fundamenta Informaticae
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Traditional work in inductive inference has been to model a learner receiving data about a function f and trying to learn the function. The data is usually just the values f(0), f(1),…. The scenario is modeled so that the learner is also allowed to ask questions about the data (e.g., ( ∀ &khgr;) [&khgr; 17 → f(&khgr;) = 0]?). An important parameter is the language that the lerner may use to formulate queries. We show that for most languages a learner can learn more by asking questions than by passively receiving data. Mathematical tools used include the solution to Hilbert's tenth problem, the decidability of Presuburger arithmetic, and &ohgr;-automata.