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AI Enabled Bridge Bidding Supporting Interactive Visualization.

Xiaoyu Zhang1,2, Wei Liu1, Linhui Lou1,2

  • 1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China.

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Summary
This summary is machine-generated.

This study introduces a novel AI framework for bridge bidding, utilizing deep neural networks to predict bids and provide explanations. This AI system significantly enhances human-computer interaction in complex card games like bridge.

Keywords:
bridge biddingdeep neural networkimperfect informationinteractive visualization

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Area of Science:

  • Artificial Intelligence
  • Game Theory
  • Computational Intelligence

Background:

  • The advancement of AI in perfect information games has shifted focus to imperfect information games.
  • Bridge, a complex card game, presents significant research challenges in both bidding and playing phases.
  • Existing AI research in card games has not adequately addressed the intricacies of bridge bidding.

Purpose of the Study:

  • To develop an AI framework for bridge bidding that leverages deep neural networks.
  • To introduce the first bidding visualization system for bridge AI.
  • To enable intuitive human-computer interaction by providing explanations for AI bidding decisions.

Main Methods:

  • A deep neural network-based bidding model (BM) was designed.
  • A visualization system was integrated with the bidding model.
  • The framework was trained and evaluated on bridge bidding scenarios.

Main Results:

  • The proposed AI framework successfully predicts reasonable bids within established human bidding systems.
  • The system provides intuitive explanations for its bidding decisions, facilitating human understanding.
  • Experimental evaluations indicate superior performance compared to most existing bridge bidding systems.

Conclusions:

  • The developed AI framework offers a significant advancement in bridge bidding technology.
  • The integration of visualization and explanation enhances human-computer collaboration in bridge.
  • This research opens new avenues for AI applications in complex imperfect information games.