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
Caragiannis, I., Procaccia, A.D., Shah, N.: When do noisy votes reveal the truth?. In: EC, pp. 143–160. ACM, New York (2013)
Conitzer, V., Sandholm, T.: Common voting rules as maximum likelihood estimators. In: UAI, pp. 145–152 (2005)
Echenagucia, T.M., Capozzoli, A., Cascone, Y., Sassone, M.: The early design stage of a building envelope. Appl. Energy 154, 577–591 (2015)
Elkind, E., Shah, N.: Electing the most probable without eliminating the irrational: Voting over intransitive domains. In: UAI (2014)
Erhan, H., Wang, I., Shireen, N.: Interacting with thousands: A parametric-space exploration method in generative design. In: ACADIA (2014)
Gerber, D.J., Lin, S.H.E.: Designing in complexity: Simulation, integration, and multidisciplinary design optimization for architecture. Simulation 90(8), 936–959 (2014)
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)
Globa, A., Donn, M., Moloney, J.: Abstraction versus cased-based: A comparative study of two approaches to support parametric design. In: ACADIA (2014)
Haynes, G.A.: Testing the boundaries of the choice overload phenomenon. Psychol. Mark. 26(3), 204–212 (2009)
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)
Jiang, A.X., Marcolino, L.S., Procaccia, A.D., Sandholm, T., Shah, N., Tambe, M.: Diverse randomized agents vote to win. In: NIPS (2014)
Kalech, M., Kraus, S., Kaminka, G.A., Goldman, C.V.: Practical voting rules with partial information. JAAMAS 22, 151–182 (2011)
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)
van Langen, P., Brazier, F.: Design space exploration revisited. Artif. Intell. Eng. Des. Anal. Manuf. 20, 113–119 (2006)
List, C., Goodin, R.E.: Epistemic democracy: generalizing the condorcet jury theorem. J. Polit. Philos. 9, 277–306 (2001)
Mao, A., Procaccia, A.D., Chen, Y.: Better human computation through principled voting. In: AAAI (2013)
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)
Nurmi, H.: Comparing Voting Systems. Springer, Heidelberg (1987)
Polikar, R.: Ensemble learning. In: Zhang, C., Ma, Y. (eds.) Ensemble Machine Learning: Methods and Applications, pp. 1–34. Springer, Heidelberg (2012)
Procaccia, A.D., Reddi, S.J., Shah, N.: A maximum likelihood approach for selecting sets of alternatives. In: UAI (2012)
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)
Snooks, R.: Encoding behavioral matter. In: Proceedings of the International Symposium on Algorithmic Design for Architecture and Urban Design. ALGODE (2011)
Vehlken, S.: Computational swarming: A cultural technique for generative architecture. Footprint - Delft Archit. Theor. J. 15 (2014)
Welch, C., Moloney, J., Moleta, T.: Selective interference: Emergent complexity informed by programmatic, social and performative criteria. In: ACADIA (2014)
Woodbury, R.F., Burrow, A.L.: Whither design space? Artif. Intell. Eng. Des. Anal. Manuf. 20, 63–82 (2006)
Xia, L., Conitzer, V.: A maximum likelihood approach towards aggregating partial orders. In: IJCAI (2011)
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)
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)
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This research is supported by MURI grant W911NF-11-1-0332, and the National Science Foundation under grant 1231001.
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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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