The content delves into the challenges of preserving transitivity in game ratings, introducing a novel approach using neural networks for game decomposition. It discusses the significance of sign-rank, potential games, and basis functions in understanding transitive relations among strategies. The methodology is evaluated on various games, showcasing improved accuracy in capturing the sign of games.
The Elo rating system's limitations are highlighted, leading to the development of a Hyperbolic Elo rating that preserves transitivity. The concept of ordinal potential games is introduced to characterize transitive games effectively. The architecture for learning game decompositions emphasizes capturing the sign pattern efficiently.
The discussion extends to online updates of Hyperbolic Elo ratings and potential-based ratings, suggesting future research directions. The paper concludes with acknowledgments and references to related works.
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by Nelson Vador... a las arxiv.org 03-07-2024
https://arxiv.org/pdf/2306.05366.pdfConsultas más profundas