Outer-Learning Framework for Playing Multi-Player Trick-Taking Card Games: A Case Study in Skat
This work addresses the challenge of enhancing AI performance in complex card games for game developers and AI researchers, though it is incremental as it builds on existing statistical methods by adding self-play data.
The paper tackled the problem of early decision-making in multi-player trick-taking card games like Skat, where statistical data from human games is limited, by developing an outer-learning framework that expands the database with AI-generated games to improve prediction accuracy, resulting in a self-improving engine that supports various game decisions.
In multi-player card games such as Skat or Bridge, the early stages of the game, such as bidding, game selection, and initial card selection, are often more critical to the success of the play than refined middle- and end-game play. At the current limits of computation, such early decision-making resorts to using statistical information derived from a large corpus of human expert games. In this paper, we derive and evaluate a general bootstrapping outer-learning framework that improves prediction accuracy by expanding the database of human games with millions of self-playing AI games to generate and merge statistics. We implement perfect feature hash functions to address compacted tables, producing a self-improving card game engine, where newly inferred knowledge is continuously improved during self-learning. The case study in Skat shows that the automated approach can be used to support various decisions in the game.