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：合作博弈理论在当代人工智能领域具有广泛应用，包括可解释机器学习、资源分配和协作决策等场景。然而，定义合作博弈需要为指数级数量的联盟赋值，实践中即便获取单个联盟的值也可能耗费大量资源。若简单隐藏某些联盟的值，会导致个体对集体大联盟的贡献存在歧义性。这种歧义性往往使参与者因内在偏见或策略考量而持有过度乐观的预期，最终导致集体诉求总和超过实际的大联盟价值。本文提出了一种优化联盟值揭示序列的框架，其核心目标是在合作博弈中高效弥合参与者预期与可实现结果之间的差距。我们的贡献包含三方面：(i) 研究个体参与者对缺失联盟值的乐观补全方案及其产生的差距，并分析有助于提升优化效率的解析特性；(ii) 开发了基于已知先验的博弈类别中，通过离线或在线方式揭示额外联盟值来最小化该差距的方法；(iii) 通过实证实验展示了算法在实际场景中的性能，并探究了联盟值揭示的典型顺序。