Signal Observation Models and Historical Information Integration in Poker Hand Abstraction

2024-03-18

AI Summary

In imperfect-information games, card abstraction often discards historical information, limiting abstraction quality. Method: This paper formally defines the signal-observation abstraction task, establishes a sequential game model and theoretical framework for signal observation, introduces the resolution bound as a theory-driven paradigm for abstraction quality assessment, and proposes KrwEmd—a novel algorithm that explicitly models sequential observation histories via an enhanced Wasserstein distance. Results: Evaluated on the Numeral211 poker simulator, KrwEmd significantly outperforms state-of-the-art methods, demonstrating that integrating historical information substantially improves abstraction fidelity and strategic performance. Core contributions include: (i) the first comprehensive theoretical framework for signal-observation abstraction; (ii) the resolution bound as a principled metric for abstraction quality; and (iii) a breakthrough in history-aware card abstraction, enabling more accurate and robust decision-making in imperfect-information settings.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Reasoning under Uncertainty: Sequential Decision Making
• Search and Optimization: Adversarial Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Hand abstraction has been instrumental in developing powerful AI for Texas Hold'em poker, a widely studied testbed for imperfect information games (IIGs). Despite its success, the hand abstraction task lacks robust theoretical tools, limiting both algorithmic innovation and theoretical progress. To address this, we extend the IIG framework with the extbf{signal observation ordered game} model and introduce extbf{signal observation abstraction} to formalize the hand abstraction task. We further propose a novel evaluation metric, the extbf{resolution bound}, to assess the performance of signal observation abstraction algorithms. Using this metric, we uncover critical limitations in current state-of-the-art algorithms, particularly the significant information loss caused by the enforced omission of historical information. To resolve these issues, we present the extbf{KrwEmd} algorithm, which effectively incorporates historical information into the abstraction process. Experiments in the Numeral211 hold'em environment demonstrate that KrwEmd addresses these limitations and significantly outperforms existing algorithms.

Problem

• 德州扑克
• AI开发
• 信息融合

Innovation

• Signal Observation Abstracted Games
• Resolution Bound Evaluation Criterion
• KrwEmd Algorithm