学习在部分观察域中提供什么信息
Learning What Information to Give in Partially Observed Domains
摘要：
在许多机器人应用中，自主智能体必须在其人类队友未观察到的部分观察到的环境中行动并探索该环境。我们考虑这样一种设置，在这种设置中，代理可以在行动时向人类传输声明性信息，帮助他们理解这个看不见的环境的各个方面。在这项工作中，我们解决的算法问题，代理应该如何计划采取什么行动，什么信息传输。自然地，人们会期望人类有偏好，我们在理论上通过对传输的信息进行评分来对信息进行建模，该信息基于它在人类信仰状态的加权熵中引起的变化。我们制定这个设置为一个信念MDP，并给出了一个易于处理的算法来解决它近似。然后，我们给出了一个算法，允许智能体通过探索在线学习人类的偏好。我们验证我们的方法在模拟离散和连续部分观察搜索和恢复域的实验。访问http://tinyurl.com/chitnis-corl-18获取补充视频。
英文摘要：
In many robotic applications, an autonomous agent must act within and explore a partially observed environment that is unobserved by its human teammate. We consider such a setting in which the agent can, while acting, transmit declarative information to the human that helps them understand aspects of this unseen environment. In this work, we address the algorithmic question of how the agent should plan out what actions to take and what information to transmit. Naturally, one would expect the human to have preferences, which we model information-theoretically by scoring transmitted information based on the change it induces in weighted entropy of the human's belief state. We formulate this setting as a belief MDP and give a tractable algorithm for solving it approximately. Then, we give an algorithm that allows the agent to learn the human's preferences online, through exploration. We validate our approach experimentally in simulated discrete and continuous partially observed search-and-recover domains. Visit http://tinyurl.com/chitnis-corl-18 for a supplementary video.
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。
