在偏序集上决斗的诱饵强盗
Decoy Bandits Dueling on a Poset
摘要：
本文讨论了定义在偏序集上的强盗决斗问题。在这种情况下，臂可能不具有可比性，并且可能存在几个（不可比的）最佳臂。我们提出了一个算法，UnchainedBandits，有效地找到一组最佳武器的任何偏序集，即使对可比的武器不能区分从对不可比的武器，一组最小的假设。该算法依赖于诱饵的概念，这源于社会心理学。对于更容易的情况下，不可比性的信息可能是可访问的，我们提出了第二个算法，切片Bandits，它利用这些信息，并实现了非常显着的性能增益相比UnchainedBandits。我们为这两种算法提供了理论保证和实验评估。
英文摘要：
We adress the problem of dueling bandits defined on partially ordered sets, or posets. In this setting, arms may not be comparable, and there may be several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms of any poset even when pairs of comparable arms cannot be distinguished from pairs of incomparable arms, with a set of minimal assumptions. This algorithm relies on the concept of decoys, which stems from social psychology. For the easier case where the incomparability information may be accessible, we propose a second algorithm, SlicingBandits, which takes advantage of this information and achieves a very significant gain of performance compared to UnchainedBandits. We provide theoretical guarantees and experimental evaluation for both algorithms.
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。
