Robust logics

摘要

Suppose that we wish to learn from examples and counter-examples a criterion for recognizing whether an assembly of wooden blocks constitutes an arch. Suppose also that we have preprogrammed recognizers for various relationships, e.g., on-top-of(x,y), above(x,y), etc. and believe that some possibly complex expression in terms of these base relationships should suffice to approximate the desired notion of an arch. How can we formulate such a relational learning problem so as to exploit the benefits that are demonstrably available in propositional learning, such as attribute-efficient learning by linear separators, and error-resilient learning?