Skip to main content
SpringerLink
Log in

Boosting Interactive Evolution Using Human Computation Markets

  • Conference paper
Theory and Practice of Natural Computing (TPNC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8273))

Included in the following conference series:

Abstract

Interactive evolution, i.e. leveraging human input for selection in an evolutionary algorithm, is effective when an appropriate fitness function is hard to quantify yet solution quality is easily recognizable by humans. However, single-user applications of interactive evolution are limited by user fatigue: Humans become bored with monotonous evaluations. This paper explores the potential for bypassing such fatigue by directly purchasing human input from human computation markets. Experiments evolving aesthetic images show that purchased human input can be leveraged more economically when evolution is first seeded by optimizing a purely-computational aesthetic measure. Further experiments in the same domain validate a system feature, demonstrating how human computation can help guide interactive evolution system design. Finally, experiments in an image composition domain show the approach’s potential to make interactive evolution scalable even in tasks that are not inherently enjoyable. The conclusion is that human computation markets make it possible to apply a powerful form of selection pressure mechanically in evolutionary algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chou, C., Kimbrough, S., Sullivan-Fedock, J., Woodard, C., Murphy, F.: Using interactive evolutionary computation (iec) with validated surrogate fitness functions for redistricting. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, pp. 1071–1078. ACM (2012)

    Google Scholar 

  2. Clune, J., Lipson, H.: Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In: Proceedings of the European Conference on Artificial Life (2011)

    Google Scholar 

  3. den Heijer, E., Eiben, A.E.: Comparing aesthetic measures for evolutionary art. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010, Part II. LNCS, vol. 6025, pp. 311–320. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  4. Goldberg, D.E.: Simple genetic algorithms and the minimal deceptive problem. In: Davis, L.D. (ed.) Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence. Morgan Kaufmann (1987)

    Google Scholar 

  5. Gruau, F., Quatramaran, K.: Cellular encoding for interactive evolutionary robotics. In: Fourth European Conference on Artificial Life, pp. 368–377. MIT Press (1997)

    Google Scholar 

  6. Hastings, E., Stanley, K.: Interactive genetic engineering of evolved video game content. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games, p. 8. ACM (2010)

    Google Scholar 

  7. Ipeirotis, P.: Analyzing the amazon mechanical turk marketplace. XRDS: Crossroads. The ACM Magazine for Students 17(2), 16–21 (2010)

    Article  Google Scholar 

  8. Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the Twenty-sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 453–456. ACM (2008)

    Google Scholar 

  9. Kosorukoff, A.: Human based genetic algorithm. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3464–3469. IEEE (2001)

    Google Scholar 

  10. Lehman, J., Stanley, K.O.: Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation 19(2), 189–223 (2011)

    Article  Google Scholar 

  11. Lehman, J., Stanley, K.O.: Beyond open-endedness: Quantifying impressiveness. In: Proceedings of Artificial Life Thirteen, ALIFE XIII (2012)

    Google Scholar 

  12. MacCallum, R., Mauch, M., Burt, A., Leroi, A.: Evolution of music by public choice. Proceedings of the National Academy of Sciences 109(30), 12081–12086 (2012)

    Article  Google Scholar 

  13. McCormack, J.: Open problems in evolutionary music and art. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 428–436. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Miconi, T., Channon, A.: An improved system for artificial creatures evolution. In: Proceedings of the Tenth International Conference on Artificial Life (ALIFE X), pp. 255–261. MIT Press (2006)

    Google Scholar 

  15. Orkin, J., Roy, D.: The restaurant game: Learning social behavior and language from thousands of players online. Journal of Game Development 3(1), 39–60 (2007)

    Google Scholar 

  16. Risi, S., Lehman, J., D’Ambrosio, D.B., Hall, R., Stanley, K.O.: Combining search-based procedural content generation and social gaming in the petalz video game. In: Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2012) (2012)

    Google Scholar 

  17. Secretan, J., Beato, N., D’Ambrosio, D., Rodriguez, A., Campbell, A., Folsom-Kovarik, J., Stanley, K.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evol. Comp. (2011) (to appear)

    Google Scholar 

  18. Stanley, K.: Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines 8(2), 131–162 (2007)

    Article  Google Scholar 

  19. Takagi, H.: Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation. Proceedings of the IEEE 89(9), 1275–1296 (2001)

    Article  Google Scholar 

  20. Von Ahn, L.: Games with a purpose. Computer 39(6), 92–94 (2006)

    Article  Google Scholar 

  21. Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: Recaptcha: Human-based character recognition via web security measures. Science 321(5895), 1465–1468 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  22. Woolley, B.G., Stanley, K.O.: Exploring promising stepping stones by combining novelty search with interactive evolution. CoRR abs/1207.6682 (2012)

    Google Scholar 

  23. Yu, L., Nickerson, J.: An internet-scale idea generation system. ACM Transactions on Interactive Intelligent Systems 3(1), 2013 (2011)

    Google Scholar 

  24. Yu, L., Nickerson, J.V.: Cooks or cobblers?: crowd creativity through combination. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1393–1402. ACM (2011)

    Google Scholar 

  25. Zaera, N., Cliff, D., Bruten, J.: (Not) evolving collective behaviours in synthetic fish. In: From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press Bradford Books (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Texas at Austin, USA

    Joel Lehman & Risto Miikkulainen

Authors
  1. Joel Lehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Risto Miikkulainen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Avinguda Catalunya, 35, 43002, Tarragona, Spain

    Adrian-Horia Dediu

  2. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Avinguda Catalunya, 35, 43002, Tarragona,, Spain

    Carlos Martín-Vide

  3. Fakultät für Informatik, Institut für Wissens- und Sprachverarbeitung, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Bianca Truthe

  4. Escuela Politécnica, Department of Technologies of Computers and Communications, (TC2), University of Extremadura, Campus Universitario s/n, 10003, Cáceres, Spain

    Miguel A. Vega-Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lehman, J., Miikkulainen, R. (2013). Boosting Interactive Evolution Using Human Computation Markets. In: Dediu, AH., Martín-Vide, C., Truthe, B., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2013. Lecture Notes in Computer Science, vol 8273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45008-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-45008-2_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45007-5

  • Online ISBN: 978-3-642-45008-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation