In this note, I introduce a new framework called n-person games with partial knowledge, in which players have only limited knowledge about the aspects of the game -- including actions, outcomes, and other players. For example, playing an actual game of chess is a game of partial knowledge. To analyze these games, I introduce a set of new concepts and mechanisms for measuring the intelligence of players, with a focus on the interplay between human- and machine-based decision-making. Specifically, I introduce two main concepts: firstly, the Game Intelligence (GI) mechanism, which quantifies a player's demonstrated intelligence in a game by considering not only the game's outcome but also the "mistakes" made during the game according to the reference machine's intelligence. Secondly, I define gaming-proofness, a practical and computational concept of strategy-proofness. The GI mechanism provides a practicable way to assess players and can potentially be applied to a wide range of games, from chess and backgammon to AI systems. To illustrate the concept, I apply the GI mechanism to a selection of top-level chess games.

翻译：在本文中，我提出了一种称为n人部分知识博弈的新框架，其中参与者对博弈的各个方面（包括行动、结果及其他参与者）仅具备有限知识。例如，实际对弈国际象棋即属于部分知识博弈。为分析此类博弈，我引入了一系列衡量参与者智能的新概念与机制，重点关注人类决策与机器决策之间的相互作用。具体而言，我提出两个核心概念：首先，博弈智能（GI）机制，该机制不仅考虑博弈结果，还参考机器智能对博弈过程中出现的"失误"进行量化评估，从而衡量参与者在博弈中展现的智能水平；其次，我定义了博弈抗操纵性——一种兼具实用性与计算性的策略抗操纵性概念。GI机制为评估参与者提供了可行方法，可广泛应用于从国际象棋、双陆棋到人工智能系统等各类博弈。为阐释该概念，我将GI机制应用于若干顶级国际象棋对局分析中。