提出Minibal算法，让AI对弈时既不碾压也不轻易认输。
Minibal: Balanced Game-Playing Without Opponent Modeling
- 基于最小化最大值思想改进算法，专注寻找平衡策略。
- 在7个棋类游戏中平均对局结果接近完美平衡。
- 适合用于人机对战游戏，提升趣味性和教学价值。
近期游戏人工智能（如AlphaZero和Athénan）在多种棋类游戏中达到超人类水平。然而，这些智能体在与人类对弈时往往持续压制对手，难以带来乐趣或教育意义。本文针对平衡对弈问题，提出一种名为Minibal（最小化并平衡）的极小极大算法变体，专门设计用于实现对抗中的平衡性。在此基础上，我们对无界极小极大算法进行了多项改进，旨在发现更具平衡性的策略。在七个棋类游戏上的实验表明，其中一种变体能持续实现最接近理想平衡的对局结果，平均表现接近完美平衡。该成果为构建既具挑战性又具吸引力的人机对弈系统提供了可行基础，适用于娱乐及严肃游戏场景。
原文摘要 · Abstract (English)
Recent advances in game AI, such as AlphaZero and Athénan, have achieved superhuman performance across a wide range of board games. While highly powerful, these agents are ill-suited for human-AI interaction, as they consistently overwhelm human players, offering little enjoyment and limited educational value. This paper addresses the problem of balanced play, in which an agent challenges its opponent without either dominating or conceding. We introduce Minibal (Minimize & Balance), a variant of Minimax specifically designed for balanced play. Building on this concept, we propose several modifications of the Unbounded Minimax algorithm explicitly aimed at discovering balanced strategies. Experiments conducted across seven board games demonstrate that one variant consistently achieves the most balanced play, with average outcomes close to perfect balance. These results establish Minibal as a promising foundation for designing AI agents that are both challenging and engaging, suitable for both entertainment and serious games.
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