解释并改进多智能体决策中的信息互补性
Explaining and Improving Information Complementarities in Multi-Agent Decision-making
AI总结：
本文提出了一种新的解释技术ILIV-SHAP，用于改进多智能体决策中的信息互补性，通过人机协作提升决策准确性。
摘要：
多个智能体越来越多地被结合在一起进行决策，期望通过互补性的表现来实现超越个体决策的性能。然而，了解如何提高协作智能体的性能需要了解每个智能体所使用的信息和策略。本文聚焦于人机配对，贡献了一个决策理论框架，用于表征信息的价值。通过定义互补信息，我们的方法识别了智能体更好地利用可用信息的机会。我们提出了一种新的解释技术（ILIV-SHAP），它将SHAP解释方法适应于突出人类补充信息。我们通过人机决策研究验证了框架和ILIV-SHAP的有效性，并在胸片诊断和深度伪造检测示例上展示了框架。我们发现，使用ILIV-SHAP与AI预测相比，非AI辅助决策在错误率上的减少更加可靠。
英文摘要：
Multiple agents are increasingly combined to make decisions with the expectation of achieving complementary performance, where the decisions they make together outperform those made individually. However, knowing how to improve the performance of collaborating agents requires knowing what information and strategies each agent employs. With a focus on human-AI pairings, we contribute a decision-theoretic framework for characterizing the value of information. By defining complementary information, our approach identifies opportunities for agents to better exploit available information in AI-assisted decision workflows. We present a novel explanation technique (ILIV-SHAP) that adapts SHAP explanations to highlight human-complementing information. We validate the effectiveness of our framework and ILIV-SHAP through a study of human-AI decision-making, and demonstrate the framework on examples from chest X-ray diagnosis and deepfake detection. We find that presenting ILIV-SHAP with AI predictions leads to reliably greater reductions in error over non-AI assisted decisions more than vanilla SHAP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
