Justified Generalization: Acquiring Procedures from Examples.

This thesis describes an implemented system called NODDY for acquiring procedures from examples presented by a teacher. Acquiring procedures from examples involves several different generalization tasks. Generalization is an underconstrained task, and the main issue of machine learning is how to deal with this underconstraint. The thesis presents two principles is to exploit domain based constraints. The second principle is to aviod spurious generalizations be requiring justification before adapting a generalization. NODDY demonstrates several different ways of justifying a generalization and proposes a way of ordering and searching a space of candidate generalizations based on how much evidence would be required to justify each generalization. Acquiring procedures also involves three types of constructive generalization inferring loops a kind of group, inferring complex reactions and state variables, and inferring predicates. NODDY demonstrates three constructive generalization for these kinds of generalization. Keywords Machine learning, Constraining generalization, Justification of generalization.