How Models of Creativity and Analogy Need to Answer the Tailorability Concern

Creativity and the ability to reason analogically have a strong relationship [18, 19, 29, 23]. For example, many of the greatest lines in literature make use of familiar analogical processes: “A rose by any other name would smell as sweet” only makes sense if that rose and its name are understood to correspond to Romeo and his family name (analogical mapping). The subsequent analogical inference is that just as the nature of the rose is independent of its name, so is Romeo’s nature independent of his. Even if one believes that overuse of analogy can be harmful to creative thought [40], many researchers argue that the ability to determine analogical similarity is important at least for combinatorial creativity, which seems to be very easy for humans but very difficult for AI systems [5, 6]. Furthermore, the importance of analogy is of course not limited to creativity, as analogical ability has been identified as an indispensable component of artificial general intelligence as well [11, 27]. With the above in mind, it makes sense to develop models of analogy, both computational and theoretical. Much work has been done in this direction; a few implementations include SME [17], LISA [30], HDTP [26], and recently our own META-R [33]. On the surface, it seems that the current generation of analogical systems sufficiently capture and explain all of the phenomena commonly associated with analogical reasoning, and that they will eventually reach levels characteristic of human cognition. It may well be the case that the most important principles underlying the nature of analogy have been expressed. But a serious objection has been