论文标题
通过直接提问模型启发
Model Elicitation through Direct Questioning
论文作者
论文摘要
未来将充斥着人类是机器人在复杂环境中共同努力的场景。队友互动，机器人的互动必须是获取有关人类（队友）模型的有用信息。在机器人相互作用之前，存在许多挑战，例如在人类模型中纳入结构差异，确保更简单的响应等。在本文中，我们研究了机器人如何相互作用以从一组模型中定位人类模型。我们展示了如何产生问题来完善机器人对队友模型的理解。我们在各种计划域中评估该方法。评估表明，这些问题可以离线产生，并可以通过简单的答案帮助完善模型。
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.
