Proceedings of the Annual Meeting of the Cognitive Science Society

The robustness of analogical transfer based on the A C M E modeling of mapping by constraint satisfaction (Holyoak & Thagard, 1989) was investigated in a series of computational experiments using Hinton's (1986) "family tree" problem. Propositions were deleted randomly from the full representations of either both analogs (descriptions of an English and an Italian family) or just the target, and after mapping a "copy with substitutions" procedure was used to generate transfer propositions intended to restore the full representational structures. If as many as 5 0 % of the propositions in the target analog were deleted, the system was able to recreate all of the missing information without error; significant recovery was obtained even if as many as 8 0 % of the target propositions were deleted. Robustness was only slightly reduced when the two analogs lacked any similar predicates, so that mapping depended solely on structural constraints. Transfer was much more impaired when deletions were made from both analogs, rather than just the target. The results indicate that for isomorphic representations, analogical transfer by constraint satisfaction can exceed the regenerative capacity of general learning algorithms, such as back-propagation.