通过提出足够多的问题来确定胜者的艺术：基于次优查询的竞争性偏好引出
The Art of Calling the Winner by Asking Just Enough Questions: Competitive Preference Elicitation with Next-Best Queries
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中文总结 AI 辅助
该研究针对集体选择的偏好引出问题，提出次优查询模型，对不同投票规则的竞争比进行理论分析，为Borda计数开发两种技术并验证了层级剪枝的实际效果。
中文摘要 AI 辅助
我们研究主动引出智能体偏好的问题，以使用主流投票规则在m个备选方案中进行集体选择，重点关注次优查询模型，其中智能体通过揭示其下一个最偏好的备选方案来响应查询，我们测量竞争比，即主动引出算法所做查询数量与事后揭示胜者备选方案所需最小查询数量之间的最坏情况比率。我们表明，对于许多位置评分规则可以实现亚线性竞争比，而每个孔多塞一致性规则的竞争比与m成线性关系。对于Borda计数，我们开发了两种互补技术：层级剪枝（其分析可扩展到一般凹评分规则）和多尺度评分阈值，这为Borda提供了O(√m)的最坏情况保证。我们还在真实数据上证明了层级剪枝的出色经验性能。
英文摘要
We study active elicitation of agent preferences for collectively choosing among $m$ alternatives using prominent voting rules. We focus on the next-best query model, in which an agent responds to a query by revealing their next favorite alternative, and measure the competitive ratio, which is the worst-case ratio between the number of queries made by the active elicitation algorithm and the minimum number of queries needed to reveal the winning alternative(s) in hindsight. We show that sublinear competitive ratios are achievable for many positional scoring rules, whereas every Condorcet-consistent rule has competitive ratio linear in $m$. For Borda count, we develop two complementary techniques: level-wise pruning, whose analysis extends to general concave scoring rules, and multi-scale score thresholding, which gives an $O(\sqrt m)$ worst-case guarantee for Borda. We also demonstrate strong empirical performance of level-wise pruning on real data.
发表机构
- University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。
