  The synthesis of string transformation programs from input-output examples
utilizes various techniques, all based on an inductive bias that comprises a
restricted set of basic operators to be combined. A new algorithm, Transduce,
is proposed, which is founded on the construction of abstract transduction
grammars and their generalization. We experimentally demonstrate that Transduce
can learn positional transformations efficiently from one or two positive
examples without inductive bias, achieving a success rate higher than the
current state of the art.
