Minibal: Balanced Game-Playing Without Opponent Modeling
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. ...
| Authors | Quentin Cohen-Solal, Tristan Cazenave |
|---|---|
| Primary category | cs.AI |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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