Heuristics for empirical discovery
Computational models of learning
Rapid construction of algebraic axioms from samples
Theoretical Computer Science - Images of programming dedicated to the memory of Andrei P. Ershov
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Towards Efficient Inductive Synthesis of Expressions from Input/Output Examples
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Incorporating Hypothetical Knowledge into the Process of Inductive Synthesis
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
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The problem of practically feasible inductive inference of functions or other objects that can be described by means of an attribute grammar is studied in this paper. In our approach based on attribute grammars various kinds of knowledge about the object to be found can be encoded, ranging from usual input/output examples to assumptions about unknown object's syntactic structure to some dynamic object's properties. We present theoretical results as well as describe the architecture of a practical inductive synthesis system based on theoretical findings.