通过直接提问进行模型启发
Model Elicitation through Direct Questioning
摘要：
未来将充满人类和机器人在复杂环境中共同工作的场景。队友之间会相互作用，而机器人的相互作用必须是关于人类（队友）模型的有用信息。有许多挑战之前，机器人可以进行交互，如将在人类的模型的结构差异，确保更简单的反应等，在本文中，我们研究了机器人如何可以进行交互，本地化的人类模型从一组模型。我们展示了如何生成问题，以完善机器人的队友的模型的理解。我们评估的方法在不同的规划领域。评估表明，这些问题可以离线生成，并可以通过简单的答案帮助完善模型。
英文摘要：
The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about getting useful information about the human's (teammate's) model. There are many challenges before a robot can interact, such as incorporating the structural differences in the human's model, ensuring simpler responses, etc. In this paper, we investigate how a robot can interact to localize the human model from a set of models. We show how to generate questions to refine the robot's understanding of the teammate's model. We evaluate the method in various planning domains. The evaluation shows that these questions can be generated offline, and can help refine the model through simple answers.
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。
