Cognitive Computation

, Volume 8, Issue 2, pp 175–186 | Cite as

Distributed Divergent Creativity: Computational Creative Agents at Web Scale

Article

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.

Keywords

Creativity Divergence Similarity Web services Metaphor Twitterbots 

References

  1. 1.
    Aristotle (translator: James Hutton). Aristotle’s Poetics. New York: Norton; 1982.Google Scholar
  2. 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.Google Scholar
  3. 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.Google Scholar
  4. 4.
    Baer J. Gender differences. In: Runco MA, Pritzker SR, editors. Encyclopedia of creativity, vol. I. New York: Academic Press; 1999.Google Scholar
  5. 5.
    Brants T, Franz A. 2006. Web 1T 5-gram Ver. 1. Philadelphia: Linguistic Data Consortium.Google Scholar
  6. 6.
    Budanitsky A, Hirst G. Evaluating WordNet-based measures of lexical semantic relatedness. Comput Linguist. 2006;32(1):13–47.CrossRefGoogle Scholar
  7. 7.
    de Bono E. Lateral thinking: creativity step by step. New York: Harper & Row; 1970.Google Scholar
  8. 8.
    Erl T. SOA: Principles of service design. Prentice Hall; 2008.Google Scholar
  9. 9.
    Fellbaum C, editor. WordNet: an electronic lexical database. Cambridge: MIT Press; 1998.Google Scholar
  10. 10.
    Guilford JP. The nature of human intelligence. New York: McGraw Hill; 1967.Google Scholar
  11. 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.Google Scholar
  12. 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.Google Scholar
  13. 13.
    Karypis G. CLUTO: A clustering toolkit. Technical Report 02-017, University of Minnesota. 2002. http://www-users.cs.umn.edu/~karypis/cluto/.
  14. 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.Google Scholar
  15. 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.Google Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.Google Scholar
  18. 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.Google Scholar
  19. 19.
    Miller GA, Charles WG. Contextual correlates of semantic similarity. Lang Cogn Process. 1991;6(1):1–28.CrossRefGoogle Scholar
  20. 20.
    Ortony A. Beyond literal similarity. Psychol Rev. 1979;86:161–80.CrossRefGoogle Scholar
  21. 21.
    Mori M. On the Uncanny Valley. In: proceedings of the humanoids-2005 workshop: views of the Uncanny Valley, Tsukuba, Japan; 2005.Google Scholar
  22. 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.Google Scholar
  23. 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.Google Scholar
  24. 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.Google Scholar
  25. 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.Google Scholar
  26. 26.
    Torrance EP. Growing up creatively gifted: the 22-Year longitudinal study. Creat Child Adult Q. 1980;3:148–58.Google Scholar
  27. 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.Google Scholar
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. 30.
    Veale T. Exploding the creativity myth: the computational foundations of linguistic creativity. London: Bloomsbury Academic; 2012.Google Scholar
  31. 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.Google Scholar
  32. 32.
    Veale T. A service-oriented architecture for computational creativity. J Comput Sci Eng. 2013;7(3):159–67.CrossRefGoogle Scholar
  33. 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. 34.
    Wilson PA, Lewandowska-Tomaszczyk B. Affective robotics: modelling and testing cultural prototypes. Cogn Comput. 2014;6(4):814–40.CrossRefGoogle Scholar
  35. 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.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsUniversity College DublinDublinIreland

Personalised recommendations