马尔可夫说服过程：从零开始学习说服
Markov Persuasion Processes: Learning to Persuade from Scratch
AI总结：
该研究针对现有马尔可夫说服过程（MPPs）需假设发送方知晓接收方收益的实际落地缺陷，提出发送方无环境先验知识场景下的部分反馈学习算法，证明其遗憾值与累积说服力损失随回合数次线性增长，并给出匹配的性能下界。
摘要：
在贝叶斯说服中，掌握信息的发送方会策略性地向接收方披露信息，以说服对方采取期望的行动。近年来，越来越多的研究关注发送方与接收方进行序列交互的场景。近期，马尔可夫说服过程（MPPs）被提出，用于刻画发送方在马尔可夫环境中面对一系列短视接收方的序列场景。目前文献中研究的MPPs存在一些问题，使其无法在实际中完全落地应用，例如它们假设发送方知晓接收方的收益。我们通过研究发送方对环境一无所知的MPPs解决了这些问题。我们为发送方设计了一种在部分反馈下运行的学习算法。我们证明，该算法相对于最优信息披露策略的遗憾值随回合数呈次线性增长，学习过程中累积的说服力损失同样如此。此外，我们还给出了与我们算法的性能保证相匹配的该场景下的下界。
英文摘要：
In Bayesian persuasion, an informed sender strategically discloses information to a receiver so as to persuade them to undertake desirable actions. Recently, a growing attention has been devoted to settings in which sender and receivers interact sequentially. Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios where a sender faces a stream of myopic receivers in a Markovian environment. The MPPs studied so far in the literature suffer from issues that prevent them from being fully operational in practice, e.g., they assume that the sender knows receivers' rewards. We fix such issues by addressing MPPs where the sender has no knowledge about the environment. We design a learning algorithm for the sender, working with partial feedback. We prove that its regret with respect to an optimal information-disclosure policy grows sublinearly in the number of episodes, as it is the case for the loss in persuasiveness cumulated while learning. Moreover, we provide a lower bound for our setting matching the guarantees of our algorithm.
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。
