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Distributed Divergent Creativity: Computational Creative Agents at Web Scale

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Abstract

Divergence is a multifaceted capability of multifaceted creative individuals. It may be exhibited to different degrees, and along different dimensions, from one individual to another. The same may be true of computational creative agents: Such systems may do more than exhibit differing levels of divergence: They may also implement the mechanics of divergence in very different ways. We argue that creative capabilities such as divergence are best viewed as cognitive services that may be called upon by cognitive agents to complete tasks in ways that may be deemed “original” or to generate products that may be deemed “creative.” We further argue that in a computational embodiment of such an agent, cognitive services are best realized as modular, distributed Web services which hide the complexities of their particular implementations and which can be discovered, reused and composed as desired by other Web-aware systems with diverse creative needs of their own. We describe the workings of one such reusable service for generating divergent categorizations on demand and show how this service can be composed with others to support the generation and rendering of novel metaphors in an autonomous Twitterbot system.

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References

  1. Aristotle (translator: James Hutton). Aristotle’s Poetics. New York: Norton; 1982.

    Google Scholar 

  2. Agirre E, Alfonseca E, Hall K, Kravalova J, Pasca M, Soroa A. Study on Similarity and Relatedness Using distributional and WordNet-based approaches. In: proceedings of NAACL ‘09, The 2009 annual conference of the North American chapter of the association for computational linguistics; 2009. p. 19—27.

  3. Almuhareb A, Poesio M. Concept learning and categorization from the web. In: proceedings of the annual meeting of the cognitive science society. Italy, July; 2005.

  4. Baer J. Gender differences. In: Runco MA, Pritzker SR, editors. Encyclopedia of creativity, vol. I. New York: Academic Press; 1999.

  5. Brants T, Franz A. 2006. Web 1T 5-gram Ver. 1. Philadelphia: Linguistic Data Consortium.

  6. Budanitsky A, Hirst G. Evaluating WordNet-based measures of lexical semantic relatedness. Comput Linguist. 2006;32(1):13–47.

    Article  Google Scholar 

  7. de Bono E. Lateral thinking: creativity step by step. New York: Harper & Row; 1970.

    Google Scholar 

  8. Erl T. SOA: Principles of service design. Prentice Hall; 2008.

  9. Fellbaum C, editor. WordNet: an electronic lexical database. Cambridge: MIT Press; 1998.

    Google Scholar 

  10. Guilford JP. The nature of human intelligence. New York: McGraw Hill; 1967.

    Google Scholar 

  11. Han L, Finin T, McNamee P, Joshi A, Yesha Y. Improving word similarity by augmenting PMI with estimates of Word polysemy. IEEE Trans Data Knowl Eng. 2012.

  12. Jiang JY, Conrath DW. Semantic similarity based on corpus statistics and lexical taxonomy. In: proceedings of the 10th international conference on research in computational linguistics, 1997. p. 19–33.

  13. Karypis G. CLUTO: A clustering toolkit. Technical Report 02-017, University of Minnesota. 2002. http://www-users.cs.umn.edu/~karypis/cluto/.

  14. Kozareva Z, Riloff E, Hovy E. Semantic class learning from the web with hyponym pattern linkage graphs. In: proceedings of the 46th annual meeting of the ACL, 2008. p. 1048–1056.

  15. Leacock C, Chodorow M. 1998. Combining local context and WordNet similarity for word sense identification. In: Fellbaum C, editor. WordNet: an electronic lexical database; 1998. p. 265–283.

  16. Li Y, Bandar ZA, McLean D. An approach for measuring semantic similarity between words using multiple information sources. IEEE Trans Knowl Data Eng. 2003;15(4):871–82.

    Article  Google Scholar 

  17. Lin D. An information-theoretic definition of similarity. In: Proceedings of the 15th ICML, the international conference on machine learning, Morgan Kaufmann, San Francisco; 1998. p. 296–304.

  18. Lesk M. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: proceedings of ACM SigDoc, ACM; 1986. p. 24–26.

  19. Miller GA, Charles WG. Contextual correlates of semantic similarity. Lang Cogn Process. 1991;6(1):1–28.

    Article  Google Scholar 

  20. Ortony A. Beyond literal similarity. Psychol Rev. 1979;86:161–80.

    Article  Google Scholar 

  21. Mori M. On the Uncanny Valley. In: proceedings of the humanoids-2005 workshop: views of the Uncanny Valley, Tsukuba, Japan; 2005.

  22. Pederson T, Patwardhan S, Michelizzi J. WordNet::Similarity: measuring the relatedness of concepts. In: proceedings of HLT-NAACL’04 (Demonstration Papers) the 2004 annual conference of the North American chapter of the association for computational linguistics, 2004. p. 38–41.

  23. Resnick P. Using information content to evaluate semantic similarity in a taxonomy. In: proceedings of IJCAI’95, the 14th international joint conference on artificial intelligence. 1995.

  24. Seco N, Veale T, Hayes J. An intrinsic information content metric for semantic similarity in WordNet. In: Proceedings of ECAI’04, the European conference on artificial intelligence; 2004.

  25. Strube M, Ponzetto SP. WikiRelate! Computing semantic relatedness using Wikipedia. In: proceedings of AAAI-06, the 2006 conference of the association for the advancement of AI, 2006. p. 1419–1424.

  26. Torrance EP. Growing up creatively gifted: the 22-Year longitudinal study. Creat Child Adult Q. 1980;3:148–58.

    Google Scholar 

  27. Veale T. The analogical thesaurus: an emerging application at the juncture of lexical metaphor and information retrieval. In: proceedings of IAAI’03, the 15th international conference on innovative applications of AI, Acupulco, Mexico; 2003.

  28. Veale T, Li G, Hao Y. 2009. Growing finely-discriminating taxonomies from seeds of varying quality and Size. In: proceedings of EACL’09, the 12th conference of the European chapter of the association for computational linguistics, Athens, Greece; 2009. p. 835–842.

  29. Veale T. Creative language retrieval: a robust hybrid of information retrieval and linguistic creativity. In: proceedings of ACL’2011, the 49th annual meeting of the association for computational linguistics, Jeju, South Korea; 2011.

  30. Veale T. Exploding the creativity myth: the computational foundations of linguistic creativity. London: Bloomsbury Academic; 2012.

    Google Scholar 

  31. Veale T. Seeing the best and worst of everything on the web with a two-level, feature-rich affect Lexicon. In: proceedings of WWW’2012, the 21st world-wide-web conference, Lyon, France; 2012.

  32. Veale T. A service-oriented architecture for computational creativity. J Comput Sci Eng. 2013;7(3):159–67.

    Article  Google Scholar 

  33. Veale T. Linguistic readymades and creative reuse. Transactions of the SDPS. J Integ Des Process Sci. 2013;17(4):37–51.

    Google Scholar 

  34. Wilson PA, Lewandowska-Tomaszczyk B. Affective robotics: modelling and testing cultural prototypes. Cogn Comput. 2014;6(4):814–40.

    Article  Google Scholar 

  35. Wu Z, Palmer M. Verb semantics and lexical selection. In: proceedings of ACL’94, 32nd annual meeting of the association for computational linguistics, Las Cruces, New Mexico; 1994. p. 133–138.

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Authors and Affiliations

  1. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Tony Veale & Guofu Li

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  1. Tony Veale
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  2. Guofu Li
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Correspondence to Tony Veale.

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Veale, T., Li, G. Distributed Divergent Creativity: Computational Creative Agents at Web Scale. Cogn Comput 8, 175–186 (2016). https://doi.org/10.1007/s12559-015-9337-9

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