论文标题
针对人类精神状态的明确推理，针对人类机器人联合任务的强大计划
Robust Planning for Human-Robot Joint Tasks with Explicit Reasoning on Human Mental State
论文作者
论文摘要
我们考虑了人类意识的任务计划问题，在该问题中，人类机器人团队获得了共同的任务，以实现已知的目标。最近的方法通过将其建模为一个独立的，理性的代理团队来解决它，该机器人计划在两个代理商（共享）任务中计划。但是，机器人知道人类不能像人造药物一样被施用，因此它可以模仿和预测人类的决策，行动和反应。基于较早的方法，我们描述了一种解决此类问题的新方法，该方法模型并使用执行时间可观察性约定。抽象地，这种建模基于情况评估，这有助于我们的方法捕捉了个体代理人信念的演变，并预测了在实践中产生的信念差异。它决定是否以及何时需要信仰对准并通过交流实现它。这些变化改善了求解器的性能：（a）有效地使用了通信，并且（b）对于更现实和具有挑战性的问题进行了鲁棒性。
We consider the human-aware task planning problem where a human-robot team is given a shared task with a known objective to achieve. Recent approaches tackle it by modeling it as a team of independent, rational agents, where the robot plans for both agents' (shared) tasks. However, the robot knows that humans cannot be administered like artificial agents, so it emulates and predicts the human's decisions, actions, and reactions. Based on earlier approaches, we describe a novel approach to solve such problems, which models and uses execution-time observability conventions. Abstractly, this modeling is based on situation assessment, which helps our approach capture the evolution of individual agents' beliefs and anticipate belief divergences that arise in practice. It decides if and when belief alignment is needed and achieves it with communication. These changes improve the solver's performance: (a) communication is effectively used, and (b) robust for more realistic and challenging problems.
