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Multi-agent Team Formation for Design Problems

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Coordination, Organizations, Institutions, and Norms in Agent Systems XI (COIN 2015)

Abstract

Design imposes a novel social choice problem: using a team of voting agents, maximize the number of optimal solutions; allowing a user to then take an aesthetical choice. In an open system of design agents, team formation is fundamental. We present the first model of agent teams for design. For maximum applicability, we envision agents that are queried for a single opinion, and multiple solutions are obtained by multiple iterations. We show that diverse teams composed of agents with different preferences maximize the number of optimal solutions, while uniform teams composed of multiple copies of the best agent are in general suboptimal. Our experiments study the model in bounded time; and we also study a real system, where agents vote to design buildings.

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References

  1. Caragiannis, I., Procaccia, A.D., Shah, N.: When do noisy votes reveal the truth?. In: EC, pp. 143–160. ACM, New York (2013)

    Google Scholar 

  2. Conitzer, V., Sandholm, T.: Common voting rules as maximum likelihood estimators. In: UAI, pp. 145–152 (2005)

    Google Scholar 

  3. Echenagucia, T.M., Capozzoli, A., Cascone, Y., Sassone, M.: The early design stage of a building envelope. Appl. Energy 154, 577–591 (2015)

    Article  Google Scholar 

  4. Elkind, E., Shah, N.: Electing the most probable without eliminating the irrational: Voting over intransitive domains. In: UAI (2014)

    Google Scholar 

  5. Erhan, H., Wang, I., Shireen, N.: Interacting with thousands: A parametric-space exploration method in generative design. In: ACADIA (2014)

    Google Scholar 

  6. Gerber, D.J., Lin, S.H.E.: Designing in complexity: Simulation, integration, and multidisciplinary design optimization for architecture. Simulation 90(8), 936–959 (2014)

    Article  Google Scholar 

  7. Gero, J., Sosa, R.: Complexity measures as a basis for mass customization of novel designs. Environ. Plan. B: Plan. Des. 35(1), 3–15 (2008)

    Article  Google Scholar 

  8. Globa, A., Donn, M., Moloney, J.: Abstraction versus cased-based: A comparative study of two approaches to support parametric design. In: ACADIA (2014)

    Google Scholar 

  9. Haynes, G.A.: Testing the boundaries of the choice overload phenomenon. Psychol. Mark. 26(3), 204–212 (2009)

    Article  Google Scholar 

  10. Iyengar, S., Lepper, M.: When choice is demotivating: Can one desire too much of a good thing? J. Pers. Soc. Psychol. 79, 995–1006 (2000)

    Article  Google Scholar 

  11. Jiang, A.X., Marcolino, L.S., Procaccia, A.D., Sandholm, T., Shah, N., Tambe, M.: Diverse randomized agents vote to win. In: NIPS (2014)

    Google Scholar 

  12. Kalech, M., Kraus, S., Kaminka, G.A., Goldman, C.V.: Practical voting rules with partial information. JAAMAS 22, 151–182 (2011)

    Google Scholar 

  13. Knysh, D.S., Kureichik, V.M.: Parallel genetic algorithms: A survey and problem state of the art. J. Comput. Syst. Sci. Int. 49(4), 579–589 (2010)

    Article  MathSciNet  Google Scholar 

  14. van Langen, P., Brazier, F.: Design space exploration revisited. Artif. Intell. Eng. Des. Anal. Manuf. 20, 113–119 (2006)

    Google Scholar 

  15. List, C., Goodin, R.E.: Epistemic democracy: generalizing the condorcet jury theorem. J. Polit. Philos. 9, 277–306 (2001)

    Article  Google Scholar 

  16. Mao, A., Procaccia, A.D., Chen, Y.: Better human computation through principled voting. In: AAAI (2013)

    Google Scholar 

  17. Marcolino, L.S., Xu, H., Jiang, A.X., Tambe, M., Bowring, E.: Give a hard problem to a diverse team: Exploring large action spaces. In: AAAI (2014)

    Google Scholar 

  18. Nurmi, H.: Comparing Voting Systems. Springer, Heidelberg (1987)

    Book  Google Scholar 

  19. Polikar, R.: Ensemble learning. In: Zhang, C., Ma, Y. (eds.) Ensemble Machine Learning: Methods and Applications, pp. 1–34. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Procaccia, A.D., Reddi, S.J., Shah, N.: A maximum likelihood approach for selecting sets of alternatives. In: UAI (2012)

    Google Scholar 

  21. Smith, B.N., Xu, A., Bailey, B.P.: Improving interaction models for generating and managing alternative ideas during early design work. In: Graphics Interface Conference (2010)

    Google Scholar 

  22. Snooks, R.: Encoding behavioral matter. In: Proceedings of the International Symposium on Algorithmic Design for Architecture and Urban Design. ALGODE (2011)

    Google Scholar 

  23. Vehlken, S.: Computational swarming: A cultural technique for generative architecture. Footprint - Delft Archit. Theor. J. 15 (2014)

    Google Scholar 

  24. Welch, C., Moloney, J., Moleta, T.: Selective interference: Emergent complexity informed by programmatic, social and performative criteria. In: ACADIA (2014)

    Google Scholar 

  25. Woodbury, R.F., Burrow, A.L.: Whither design space? Artif. Intell. Eng. Des. Anal. Manuf. 20, 63–82 (2006)

    Google Scholar 

  26. Xia, L., Conitzer, V.: A maximum likelihood approach towards aggregating partial orders. In: IJCAI (2011)

    Google Scholar 

  27. Zavala, G.R., Nebro, A.J., Luna, F., Coello, C.A.C.: A survey of multi-objective metaheuristics applied to structural optimization. Struct. Multi. Optim. 49, 537–558 (2014)

    Article  MathSciNet  Google Scholar 

  28. Zhao, F., Li, G., Yang, C., Abraham, A., Liu, H.: A human-computer cooperative particle swarm optimization based immune algorithm for layout design. Neurocomputing 132, 68–78 (2014)

    Article  Google Scholar 

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Acknowledgments

This research is supported by MURI grant W911NF-11-1-0332, and the National Science Foundation under grant 1231001.

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Correspondence to Leandro Soriano Marcolino .

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Soriano Marcolino, L. et al. (2016). Multi-agent Team Formation for Design Problems. In: Dignum, V., Noriega, P., Sensoy, M., Sichman, J. (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems XI. COIN 2015. Lecture Notes in Computer Science(), vol 9628. Springer, Cham. https://doi.org/10.1007/978-3-319-42691-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-42691-4_20

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