Cooperative game theory has diverse applications in contemporary artificial intelligence, including domains like interpretable machine learning, resource allocation, and collaborative decision-making. However, specifying a cooperative game entails assigning values to exponentially many coalitions, and obtaining even a single value can be resource-intensive in practice. Yet simply leaving certain coalition values undisclosed introduces ambiguity regarding individual contributions to the collective grand coalition. This ambiguity often leads to players holding overly optimistic expectations, stemming from either inherent biases or strategic considerations, frequently resulting in collective claims exceeding the actual grand coalition value. In this paper, we present a framework aimed at optimizing the sequence for revealing coalition values, with the overarching goal of efficiently closing the gap between players' expectations and achievable outcomes in cooperative games. Our contributions are threefold: (i) we study the individual players' optimistic completions of games with missing coalition values along with the arising gap, and investigate its analytical characteristics that facilitate more efficient optimization; (ii) we develop methods to minimize this gap over classes of games with a known prior by disclosing values of additional coalitions in both offline and online fashion; and (iii) we empirically demonstrate the algorithms' performance in practical scenarios, together with an investigation into the typical order of revealing coalition values.

翻译：合作博弈论在当代人工智能领域具有广泛的应用，包括可解释机器学习、资源分配和协同决策等方向。然而，完整描述一个合作博弈需要为指数级数量的联盟分配价值，而在实践中即使获取单个联盟的价值也可能耗费大量资源。若直接省略部分联盟价值的披露，则会导致个体对整体大联盟贡献的模糊性。这种模糊性往往使参与者——无论是出于固有偏见还是策略性考量——产生过度乐观的预期，常常导致集体诉求超过实际大联盟价值。本文提出一个旨在优化联盟价值披露顺序的框架，其核心目标是在合作博弈中高效缩小参与者预期与实际可达成果之间的差距。我们的贡献包含三个方面：（i）研究个体参与者对缺失联盟价值博弈的乐观补全方式及其产生的差距，并分析其有助于提升优化效率的解析特性；（ii）针对具有已知先验的博弈类别，通过以离线和在线两种方式披露额外联盟的价值，开发最小化该差距的方法；（iii）通过实证研究展示算法在实际场景中的性能，并对联盟价值披露的典型顺序进行探究。