Supporting Architectural Design Process with FLEA

A Distributed AI Methodology for Retrieval, Suggestion, Adaptation, and Explanation of Room Configurations
  • Viktor EisenstadtEmail author
  • Christoph Lanhgenhan
  • Klaus-Dieter Althoff
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)


The artificial intelligence methods, such as case-based reasoning and artificial neural networks were already applied to the task of architectural design support in a multitude of specific approaches and tools. However, modern AI trends, such as Explainable AI (XAI), and additional features, such as providing contextual suggestions for the next step of the design process, were rarely considered an integral part of these approaches or simply not available. In this paper, we present an application of a distributed AI-based methodology FLEA (Find, Learn, Explain, Adapt) to the task of room configuration during the early conceptual phases of architectural design. The implementation of the methodology in the framework MetisCBR applies CBR-based methods for retrieval of similar floor plans to suggest possibly inspirational designs and to explain the returned results with specific explanation patterns. Furthermore, it makes use of a farm of recurrent neural networks to suggest contextually suitable next configuration steps and to present design variations that show how the designs may evolve in the future. The flexibility of FLEA allows for variational use of its components in order to activate the currently required modules only. The methodology was initialized during the basic research project Metis (funded by German Research Foundation) during which the architectural semantic search patterns and a family of corresponding floor plan representations were developed. FLEA uses these patterns and representations as the base for its semantic search, explanation, next step suggestion, and adaptation components. The methodology implementation was iteratively tested during quantitative evaluations and user studies with multiple floor plan datasets.


Room configuration Distributed AI Case-based reasoning Neural networks Explainable AI 


  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Ahmed, S., Weber, M., Liwicki, M., Langenhan, C., Dengel, A., Petzold, F.: Automatic analysis and sketch-based retrieval of architectural floor plans. Pattern Recogn. Lett. 35, 91–100 (2014)CrossRefGoogle Scholar
  3. 3.
    Anumba, C., Ren, Z., Ugwu, O.: Agents and Multi-agent Systems in Construction. Routledge, Abingdon (2007)CrossRefGoogle Scholar
  4. 4.
    Ayzenshtadt, V., Espinoza-Stapelfeld, C.A., Langenhahn, C., Althoff, K.D.: Multi-agent-based generation of explanations for retrieval results within a case-based support framework for architectural design. In: ICAART 2018. Scitepress (2018)Google Scholar
  5. 5.
    Ayzenshtadt, V., Langenhan, C., Bukhari, S., Althoff, K.-D., Petzold, F., Dengel, A.: Extending the flexibility of case-based design support tools: a use case in the architectural domain. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 46–60. Springer, Cham (2017). Scholar
  6. 6.
    Ayzenshtadt, V., Langenhan, C., Bukhari, S.S., Althoff, K.D., Petzold, F., Dengel, A.: Distributed domain model for the case-based retrieval of architectural building designs. In: Petridis, M., Roth-Berghofer, T., Wiratunga, N. (eds.) UKCBR-2015, Cambridge, United Kingdom, 15–17 December (2015)Google Scholar
  7. 7.
    Ayzenshtadt, V., Langenhan, C., Bukhari, S.S., Althoff, K.D., Petzold, F., Dengel, A.: Thinking with containers: a multi-agent retrieval approach for the case-based semantic search of architectural designs. In: Filipe, J., van den Herik, J. (eds.) ICAART-2016, Rome, Italy, 24–26 February. SCITEPRESS (2016)Google Scholar
  8. 8.
    Cassens, J., Kofod-Petersen, A.: Designing explanation aware systems: the quest for explanation patterns. In: ExaCt, pp. 20–27 (2007)Google Scholar
  9. 9.
    Cavieres, A., Bhatia, U., Joshi, P., Zhao, F., Ram, A.: CBArch: a case-based reasoning framework for conceptual design of commercial buildings. In: Artificial Intelligence and Sustainable Design - Papers from the AAAI 2011 Spring Symposium (SS-11-02), pp. 19–25 (2011)Google Scholar
  10. 10.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP-2014. Association for Computational Linguistics (2014)Google Scholar
  11. 11.
    Chu, C.H., Wu, P.H., Hsu, Y.C.: Multi-agent collaborative 3D design with geometric model at different levels of detail. Robot. Comput. Integr. Manuf. 25(2), 334–347 (2009)CrossRefGoogle Scholar
  12. 12.
    Eisenstadt, V., Althoff, K.D.: ‘what is the next step?’ Supporting architectural room configuration process with case-based reasoning and recurrent neural networks. In: FLAIRS 2019 (2019)Google Scholar
  13. 13.
    Eisenstadt, V., Espinoza-Stapelfeld, C., Mikyas, A., Althoff, K.-D.: Explainable distributed case-based support systems: patterns for enhancement and validation of design recommendations. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 78–94. Springer, Cham (2018). Scholar
  14. 14.
    Espinoza-Stapelfeld, C.: Case-based classification of explanation expressions in search results of a retrieval system for support of conceptual phase in architecture (2018)Google Scholar
  15. 15.
    Espinoza-Stapelfeld, C., Eisenstadt, V., Althoff, K.-D.: Comparative quantitative evaluation of distributed methods for explanation generation and validation of floor plan recommendations. In: van den Herik, J., Rocha, A.P. (eds.) ICAART 2018. LNCS (LNAI), vol. 11352, pp. 46–63. Springer, Cham (2019). Scholar
  16. 16.
    Gerber, D.J., Pantazis, E., Marcolino, L.S.: Design agency. In: Celani, G., Sperling, D.M., Franco, J.M.S. (eds.) CAAD Futures 2015. CCIS, vol. 527, pp. 213–235. Springer, Heidelberg (2015). Scholar
  17. 17.
    González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., Corchado, J.: Energy optimization using a case-based reasoning strategy. Sensors 18(3), 865 (2018)CrossRefGoogle Scholar
  18. 18.
    Harbers, M., van den Bosch, K., Meyer, J.J.C.: Design and evaluation of explainable BDI agents. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 125–132 (2010)Google Scholar
  19. 19.
    Lai, I.C.: Dynamic idea maps: a framework for linking ideas with cases during brainstorming. Int. J. Architectural Comput. 3(4), 429–447 (2005)CrossRefGoogle Scholar
  20. 20.
    Langenhan, C.: A federated information system for the support of topological BIM-based approaches. Forum Bauinformatik, Aachen (2015)Google Scholar
  21. 21.
    Langenhan, C., Petzold, F.: The fingerprint of architecture-sketch-based design methods for researching building layouts through the semantic fingerprinting of floor plans. Int. Electron. Sci. Educ. J.: Architect. Mod. Inf. Technol. 4, 13 (2010)Google Scholar
  22. 22.
    Mikyas, A.: Concept for development of an explanation component for BDI agents to support the design phase in architecture (2018)Google Scholar
  23. 23.
    Richter, K.: What a shame-why good ideas can’t make it in architecture: a contemporary approach towards the case-based reasoning paradigm in architecture. In: FLAIRS Conference (2013)Google Scholar
  24. 24.
    Sabri, Q.U., Bayer, J., Ayzenshtadt, V., Bukhari, S.S., Althoff, K.D., Dengel, A.: Semantic pattern-based retrieval of architectural floor plans with case-based and graph-based searching techniques and their evaluation and visualization. In: ICPRAM 2017, Porto, Portugal, 24–26 February (2017)Google Scholar
  25. 25.
    Sharma, D., Gupta, N., Chattopadhyay, C., Mehta, S.: DANIEL: A deep architecture for automatic analysis and retrieval of building floor plans. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 420–425. IEEE (2017)Google Scholar
  26. 26.
    Simeone, D., Cursi, S., Coraglia, U.M.: Modelling buildings and their use as systems of agents. In: eCAADe-2017 (2017)Google Scholar
  27. 27.
    Smyt, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 343–357. Springer, Heidelberg (1999). Scholar
  28. 28.
    Standke, S.: Strategical extension of similarity assessment of the retrieval module in a system for support of conceptual design phase in architecture (2018)Google Scholar
  29. 29.
    Voss, A.: Case design specialists in FABEL. In: Issues and Applications of Case-based Reasoning in Design, pp. 301–335 (1997)Google Scholar
  30. 30.
    Zimring, C.M., Pearce, M., Goel, A.K., Kolodner, J.L., Sentosa, L.S., Billington, R.: Case-based decision support: a case study in architectural design (1992)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Viktor Eisenstadt
    • 1
    Email author
  • Christoph Lanhgenhan
    • 2
  • Klaus-Dieter Althoff
    • 1
    • 3
  1. 1.Institute of Computer ScienceUniversity of HildesheimHildesheimGermany
  2. 2.Chair of Architectural InformaticsTechnical University of MunichMunichGermany
  3. 3.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany

Personalised recommendations