CoupVisor：基于回合与质疑决策支持的策略优化
文章背景与核心概要
本文介绍了 CoupVisor，这是一个专为隐信息卡牌游戏《政变》（Coup）设计的高级决策支持系统。该系统旨在解决游戏中的两大核心问题：玩家在每个回合应该采取什么行动，以及何时应该质疑对手的声明。通过将游戏事件标准化并结合角色概率与手牌数量，CoupVisor 能够精准评估声明的真实性，同时避免在无证据的开局阶段产生误判。
研究通过广泛的模拟实验表明，基于获胜导向（Win-oriented）的奖励系统在性能上显著优于传统的启发式基线和短期奖励方法。该框架打通了手动对局、对局回放、模拟、信念追踪以及机器学习策略，为复杂隐信息博弈中的策略优化提供了重要的技术参考。
Summary
CoupVisor is an advanced decision-support system designed for the hidden-information card game Coup. It assists players by determining optimal actions for each turn and deciding when to challenge an opponent's claims.
The framework standardizes game events across manual play, replays, simulations, belief tracking, and machine learning policies. By combining role likelihoods with the number of cards held by a claimant, CoupVisor accurately estimates the truthfulness of a claim—correctly avoiding false suspicions on opening moves. Through extensive simulation, the research demonstrates that employing a win-oriented reward system significantly outperforms heuristic baselines and short-term reward approaches.
Paper Metadata
- arXiv Identifier: arXiv:2608.15868
- Subjects: Artificial Intelligence (
cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) - Author: Cris Huynh
- Submitted On: August 16, 2026
- Comments: 15 Pages and 9 pages of appendix
Abstract
This paper presents CoupVisor, a decision-support system for the hidden-information card game Coup. It addresses two questions: what a player should do on each turn, and when a player should challenge an opponent's claim. The system is built around a single description of game events, which is shared across manual play, replay of recorded games, simulation, belief tracking, advisor recommendations, and learning-based policies. CoupVisor estimates the chance that a claim is truthful by combining how likely each role is with how many cards the claimant still holds, which corrects a case where the very first claim of a game was flagged as suspicious despite no evidence. We compare a rule-following advisor and several learned and heuristic players across many simulated games and different opponent styles. Our main finding is that the choice of reward, whether it rewards short-term gains or ultimately winning the game, decides which learning approach performs best, and that a win-oriented reward produces a policy that outperforms all baselines.
