Author: Bonzon P.E.
Title: Learning meta-level operators in hierarchical contexts

Abstract:
A common approach to machine learning aims at the synthesis of object-level macro-operators representing sequences of actions. We argue that a greater
generality could be achieved by learning instead meta-level operators representing sequences of partially defined (or generic) inference steps that lead to the
discovery of object-level operators or concepts. The implementation of these ideas relies on a hierarchy of formalized contexts. Inference steps are
represented as deduction traces that can be both derived from and forced back into a new kind of reflective contexts. While learning follows from hopping up
a level in this hierarchy, reuse implies hopping down again. We illustrate this approach with an example showing how to learn and apply an instance of a
rule involving the compilation of heterogeneous knowledge.

In: Proceedings of the International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97), Rio de Janeiro, Brazil, February
4-6, Federal University of Rio de Janeiro Ed., pp. 177-188.

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