In the evolving landscape of human-centered AI, fostering a synergistic relationship between humans and AI agents in decision-making processes stands as a paramount challenge. This work considers a problem setup where an intelligent agent comprising a neural network-based prediction component and a deep reinforcement learning component provides advice to a human decision-maker in complex repeated decision-making environments. Whether the human decision-maker would follow the agent's advice depends on their beliefs and trust in the agent and on their understanding of the advice itself. To this end, we developed an approach named ADESSE to generate explanations about the adviser agent to improve human trust and decision-making. Computational experiments on a range of environments with varying model sizes demonstrate the applicability and scalability of ADESSE. Furthermore, an interactive game-based user study shows that participants were significantly more satisfied, achieved a higher reward in the game, and took less time to select an action when presented with explanations generated by ADESSE. These findings illuminate the critical role of tailored, human-centered explanations in AI-assisted decision-making.

翻译：在以人为中心的人工智能不断发展的背景下，在决策过程中促进人类与AI智能体之间的协同关系是一项至关重要的挑战。本研究考虑一种问题设置：一个由基于神经网络的预测组件和深度强化学习组件构成的智能体，在复杂的重复决策环境中向人类决策者提供建议。人类决策者是否会遵循智能体的建议，取决于他们对智能体的信念和信任，以及对建议本身的理解。为此，我们开发了一种名为ADESSE的方法，用于生成关于建议智能体的解释，以提高人类的信任和决策质量。在一系列具有不同模型规模的环境中进行计算实验，证明了ADESSE的适用性和可扩展性。此外，一项基于交互式游戏的用户研究表明，当参与者看到由ADESSE生成的解释时，其满意度显著提高，在游戏中获得了更高的奖励，并且选择行动所需的时间更短。这些发现阐明了定制化、以人为中心的解释在AI辅助决策中的关键作用。