论文标题
强化学习驱动的信息寻求：一种量子概率方法
Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach
论文作者
论文摘要
了解信息在互动过程中的觅食者的行为对于研究互动信息检索非常重要。尽管由于用户与信息对象〜（文本，图像等）相互作用的用户纠缠高（文本，等），但信息在不确定的信息空间中传播基本上是复杂的。但是，一般而言，信息觅食者在搜索（或觅食）替代内容时伴随着一条信息（信息饮食），通常会呈现决定性的不确定性。这种类型的不确定性类似于遵循不确定性原理的量子力学的测量。在本文中，我们将寻求信息作为一项强化学习任务。然后，我们提出了一个基于增强学习的框架，以模拟Forager Exploration，将信息觅食者视为指导其行为的代理。此外，我们的框架还结合了使用量子力学的数学形式主义的觅食者作用的固有不确定性。
Understanding an information forager's actions during interaction is very important for the study of interactive information retrieval. Although information spread in uncertain information space is substantially complex due to the high entanglement of users interacting with information objects~(text, image, etc.). However, an information forager, in general, accompanies a piece of information (information diet) while searching (or foraging) alternative contents, typically subject to decisive uncertainty. Such types of uncertainty are analogous to measurements in quantum mechanics which follow the uncertainty principle. In this paper, we discuss information seeking as a reinforcement learning task. We then present a reinforcement learning-based framework to model forager exploration that treats the information forager as an agent to guide their behaviour. Also, our framework incorporates the inherent uncertainty of the foragers' action using the mathematical formalism of quantum mechanics.
