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February 20, 2021
February 01, 2021
Programlama pratik yaparak öğrenilir: Kumbara Dergisi Röportaj
İş Bankası, Koç Üniversitesi işbirliğinde ülkemizin bilimsel ve akademik faaliyetlerine katkıda bulunmak amacıyla “Yapay Zekâ Uygulama ve Araştırma Merkezi” kurdu. Bu merkezle yapay zekâ alanında ileri düzeyde çalışmalar gerçekleştirilmesi amaçlanıyor. Kumbara Dergisi olarak, Koç Üniversitesi İş Bankası Yapay Zekâ Uygulama ve Araştırma Merkezi Direktörü Prof. Dr. Deniz Yuret’e yapay zekâ alanı ve Yapay Zekâ Uygulama ve Araştırma Merkezi hakkında sorular sorduk.
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December 31, 2020
December 29, 2020
Ulaş Sert, M.S. 2020
Contact info:
M.S Thesis: Training a Bridge Bidding Agent using Minimal Feature Engineering and Deep Reinforcement Learning, Koç University, Department of Computer Engineering. December 2020. (PDF, Presentation, Code).
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M.S Thesis: Training a Bridge Bidding Agent using Minimal Feature Engineering and Deep Reinforcement Learning, Koç University, Department of Computer Engineering. December 2020. (PDF, Presentation, Code).
Thesis Abstract:
The game of contract bridge, or just bridge, is a four-player imperfect information
card game where two partnerships of two players compete against each other. It has
two main phases: bidding and play. While the computer players have approached
human-level performance two decades ago in the playing phase, bidding is still a
very challenging problem. This makes bridge one of the last popular games where
computers still lag behind the expert human-level performance. During bidding,
players only know their own cards while participating in a public auction. Performing well in this phase requires the players to figure out how to communicate with
their partners using the limited vocabulary of bids to decide on a joint contract.
This communication is restricted by the strict ordering of legal bids and can be
negatively interfered by bids made by the opponent partnership. In this thesis, we
experiment with several novel architectures with minimal feature engineering and
evaluate them by using supervised training over a data set of expert-level human
games. After that, we further study different forms of deep reinforcement learning
to refine the resulting model by simulated gameplay. Lastly, we propose an oracle
evaluation metric that can measure the quality of any bidding sequence with respect
to the game-theoretical optimum.
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