RIO：知识库中用户交互的最小化
RIO: Minimizing User Interaction in Debugging of Knowledge Bases
摘要：
目前已知的最好的交互式调试系统依赖于一些元信息的故障概率，以提高其效率。然而，误导性的Meta信息可能会导致性能的急剧下降，其评估只能是后验的。因此，只要实际故障是未知的，总是有一些次优的相互作用的风险。在这项工作中，我们提出了一个强化学习策略，不断适应其行为取决于实现的性能，并最大限度地减少使用低质量的Meta信息的风险。因此，该方法适用于难以获得可靠的先验故障估计的应用场景。使用不同的现实世界的知识库，我们表明，建议的交互式查询策略是可扩展的，具有体面的反应时间，并优于平均w.r.t.基于熵和无风险的策略。需要大量的用户交互。
英文摘要：
The best currently known interactive debugging systems rely upon some meta-information in terms of fault probabilities in order to improve their efficiency. However, misleading meta information might result in a dramatic decrease of the performance and its assessment is only possible a-posteriori. Consequently, as long as the actual fault is unknown, there is always some risk of suboptimal interactions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable prior fault estimates are difficult to obtain. Using diverse real-world knowledge bases, we show that the proposed interactive query strategy is scalable, features decent reaction time, and outperforms both entropy-based and no-risk strategies on average w.r.t. required amount of user interaction.
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。
