Seven Desirable Properties for Artificial Learning Systems

Christophe Giraud-Carrier, Tony Martinez, Seven Desirable Properties for Artificial Learning Systems. Proceedings of the Seventh Florida AI Research Symposium (FLAIRS'94). ISBN 0-9620-1736-1, pp. 16–20. May 1994. PDF, 23 Kbytes.


Much effort has been devoted to understanding learning and reasoning in artificial intelligence, giving rise to a wide collection of models. For the most part, these models focus on some observed characteristic of human learning, such as induction or analogy, in an effort to emulate (and possibly exceed) human abilities. We propose seven desirable properties for artificial learning systems: incrementality, non-monotonicity, inconsistency and conflicting defaults handling, abstraction, self-organization, generalization, and computational tractability.We examine each of these properties in turn and show how their (combined) use can improve learning and reasoning, as well as potentially widen the range of applications of artificial learning systems. An overview of the algorithm PDL2, that begins to integrate the above properties, is given as a proof of concept.

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