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).

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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December 12, 2020

CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions

Tayfun Ates, Muhammed Samil Atesoglu, Cagatay Yigit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, Deniz Yuret. Shared Visual Representations in Human and Machine Intelligence (SVRHM 2020). NeurIPS Workshop. (PDF, Presentation)

Abstract: Recent advances in Artificial Intelligence and deep learning have revived the interest in studying the gap between the reasoning capabilities of humans and machines. In this ongoing work, we introduce CRAFT, a new visual question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 38K video and question pairs that are generated from 3K videos from 10 different virtual environments, containing different number of objects in motion that interact with each other. Two question categories from CRAFT include previously studied descriptive and counterfactual questions. Besides, inspired by the theory of force dynamics from the field of human cognitive psychology, we introduce new question categories that involve understanding the intentions of objects through the notions of cause, enable, and prevent. Our preliminary results demonstrate that even though these tasks are very intuitive for humans, the implemented baselines could not cope with the underlying challenges.


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