Artificial Intelligence and Analytics for Evaluating Trick-Taking Games: A Narrative Review with a Case Study on an Estimation Variant

Abstract

This review examined how artificial intelligence (AI) and analytics had been used to support the design and evaluation of trick-taking card games, using the author’s patented Estimation variant as a case lens. A narrative approach had been followed to bring together three key themes: AI methods for decision-making under hidden information, AI-driven balancing and automated playtesting, and the use of telemetry to analyze gameplay data with limited human trials. The literature had shown that search-based agents, evolutionary algorithms, and behavioural clustering were effective in simulating play, testing fairness, and evaluating balance. These methods had then been synthesized into a practical playbook for the patented Estimation variant, outlining how simulations could filter weak rule sets and guide targeted human playtests. The review concluded that this staged, data-driven process provided a transparent and reproducible pathway for evaluating novel rule changes, ensuring fairness and skill expression while protecting intellectual property.