If t is a variable x(), then the congruence rule applies, while the inductive step is straightforward.
Inductive learning is par ticularly suitable in the context of an automated design system because training data can be generated in an automated fashion.
In this paper we introduce a new approach to robust design based on the concept of inductive learning with regression trees.
The application of inductive classification to performance data for a pseudorandom sample of dugs, deriving decision trees for the choice of data structures.
An inductive inference is then made to the conclusion that certain instances, types, groups, or patterns of evils are gratuitous.
Unfortunately, one cannot hope to add an extensional conversion rule on inductive types, which would entail the computability of all isomorphisms of inductive types.
We now begin a (possibly countably transfinite) inductive construction.
We are now ready to present a collection of graph algorithms based on the inductive view of graphs.
