ABSTRACT
ACME and Copycat have been viewed as competing models of analogy making. Mitchell (1993) makes three major criticisms of ACME in arguing for Copycat’s superiority: that because ACME considers all syntactically possible mappings it is psychologically implausible and computationally infeasible; that its representations are rigid and hand-tailored for each problem; and that ACME’s representations are semantically empty. To evaluate these criticisms we applied ACME to simulating problems in the only domain addressed by Copycat, letter-string analogies such as, “If abc is changed into abd, how would you change kji in the same way?” Using representations that include only knowledge available to Copycat, ACME generated the most common solutions that people and Copycat produce. In addition, ACME was able to generate some solutions produced by people but that are impossible for Copycat, demonstrating that in some respects ACME is a more flexible analogical reasoner than is Copycat. These simulations answer each of Mitchell’s criticisms of ACME. ACME can incorporate domain-relevant knowledge to allow a principled reduction in the number of mappings considered; it can generate novel representations based on its domain-general constraints; and it can incorporate semantic content into its representations. In addition, ACME has the advantage of being applicable to many different domains.
