This paper describes a reimplementation of a function induction
algorithm that is part of Peter Andreae's robot procedure learning system NODDY.
Results are given, the implementation is compared to Andreae's version,
and the BACON and COPER systems are compared to both.
The discussion focuses on the representation of knowledge--in particular on the
representation of inverse operators and the induced expression--and the
strength of argument typing.
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