Minibal: Balanced Game-Playing Without Opponent Modeling

TL;DR

Minibal is a new Minimax-based algorithm designed to create balanced game-playing AI that neither dominates nor concedes, enhancing human-AI interaction and educational value.

Contribution

The paper introduces Minibal, a novel Minimax variant specifically aimed at achieving balanced play in board games, improving human-AI engagement.

Findings

One Minibal variant achieves near-perfect balance across seven games.

Minibal enhances human-AI interaction by providing challenging yet fair gameplay.

Experimental results show Minibal's effectiveness in creating engaging AI opponents.

Abstract

Recent advances in game AI, such as AlphaZero and Ath\'enan, 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.