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

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Computational Creativity Research: Towards Creative Machines

Part of the book series: Atlantis Thinking Machines ((ATLANTISTM,volume 7))

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Abstract

Analogy is a major component of human creativity. Tasks from the ability to generate new stories to the ability to create new and insightful mathematical theorems can be shown to at least partially be explainable in terms of analogical processes. Artificial creativity and AGI systems, then, require powerful analogical subsystems—or so we will soon briefly argue. It quickly becomes obvious that a roadblock to such a use for analogical systems is a common critique that currently applies to every one in existence: the so-called “Tailorability Concern” (TC). Unfortunately, TC currently lacks a canonical formalization, and as a result the precise conditions that must be satisfied by an analogical system intended to answer TC are unclear. We remedy this problem by developing a still-informal but clear formulation of what it means to successfully answer TC, and offer guidelines for analogical systems that hope to progress further toward AGI.

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Notes

  1. 1.

    It would be less appropriate to urge a careful treatment of the TC and tie it so closely to large semantic databases if they weren’t available. But over the past few years, natural-language processing and semantic-web technologies have been progressing to the point where we now have access to large collections of semantic databases containing wide-ranging general knowledge. These include Cyc [35], Freebase [7], and DBPedia [3]. Many of these have easy-to-use interfaces.

  2. 2.

    We might leave room here to exclude models of analogy that have psychological or neurological plausibility as their primary end goals. In these cases, it might be the goal of the model to replicate poor analogical reasoning as well, if it matches human performance. But it is our assumption (at least in the present inquiry) that the ultimate goal of AGI research is not to model poor human reasoning.

  3. 3.

    We do not mean here to say that what works best for large artificial databases is the same as what is employed by the human brain. But if a researcher declares that the structure of human knowledge has certain properties, and large datasets cannot be created that do not have those properties for reasons of scalability, then it should be at least a weak hint that perhaps the assumption of those properties is not practicable.

  4. 4.

    This is a common criticism of Hummel and Holyoak’s LISA system; see [24].

  5. 5.

    We must mention here that standard proportion-type analogy questions seem to be falling out of favor recently; they have for example been removed from the SAT, and it is unclear at this time whether they will be brought back anytime soon.

  6. 6.

    Of course, this discussion does not even mention the difficulty artificial systems have in finding an overlap between symbols on the basis of the semantics of the referents of those symbols. Such a problem is somewhat out of this chapter’s scope, but is certainly something that researchers interested in transcending the TC should eventually tackle.

  7. 7.

    In the case of automatic programming, the input shown in Fig. 5.2 would be instantiated as the informal definition of a number-theoretic function (where that definition can be partly linguistic and partly visual), and the answer is code in some conventional programming language, accompanied by a proof of the correctness of this code relative to the input. Automatic-programming systems seemingly require the ability to judge two programs analogous. More precisely, such systems seemingly would need to be able to answer this question: Given a set of programs \(\fancyscript{P}\) in some programming language, can the system produce a similarity metric \(\rho :\fancyscript{P} \times \fancyscript{P} \rightarrow \mathbb {R}\) capturing which pairs of programs are semantically analogous?

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Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, USA

    John Licato, Selmer Bringsjord & Naveen Sundar Govindarajulu

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  1. John Licato
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  2. Selmer Bringsjord
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  3. Naveen Sundar Govindarajulu
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Correspondence to John Licato .

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Editors and Affiliations

  1. Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany

    Tarek R. Besold

  2. IIIA-CSIC, Bellaterra (Barcelona), Spain

    Marco Schorlemmer

  3. CISA, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Alan Smaill

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Licato, J., Bringsjord, S., Govindarajulu, N.S. (2015). How Models of Creativity and Analogy Need to Answer the Tailorability Concern. In: Besold, T., Schorlemmer, M., Smaill, A. (eds) Computational Creativity Research: Towards Creative Machines. Atlantis Thinking Machines, vol 7. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-085-0_5

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  • DOI: https://doi.org/10.2991/978-94-6239-085-0_5

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