Solving the Double Dummy Bridge Problem with Shallow Autoencoders

  • Jacek MańdziukEmail author
  • Jakub Suchan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)


This paper presents a new approach to solving the Double Dummy Bridge Problem (DDBP). The DDBP is a hard classification task utilized by bridge playing programs which rely on Monte Carlo simulations. The proposed method employs shallow autoencoders (AEs) during an unsupervised pretraining phase and Multilayer Perceptron networks (MLPs) with three hidden layers, built on top of these trained AEs, in the final fine-tuning training. The results are compared with our previous study in which MLPs with similar architectures, but with no use of AEs and pretraining, were employed to solve this task. Several conclusions concerning efficient weight topologies and fine-tuning schemes of the proposed model, as well as interesting weight patterns discovered in the trained networks are presented and explained.


Autoencoder Double Dummy Bridge Problem Classification 



This work was supported by the Polish National Science Centre grant 2017/25/B/ST6/02061.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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