As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success. Ensuring users understand the models over which algorithms reason promotes user trust. This work seeks to reconcile differences between the reward model that an algorithm uses for online partially observable Markov decision (POMDP) planning and the implicit reward model assumed by a human user. Action discrepancies, differences in decisions made by an algorithm and user, are leveraged to estimate a user's objectives as expressed in weightings of a reward function.

翻译：随着人工智能算法在关键任务应用中的日益普及，提升用户对这些系统的信任度对其成功至关重要。确保用户理解算法所推理的模型，有助于增强用户信任。本研究旨在调和算法在在线部分可观测马尔可夫决策过程规划中使用的奖励模型，与人类用户隐含假设的奖励模型之间的差异。通过利用算法与用户决策之间的行动差异，本文基于奖励函数的权重估计用户表达的目标。