Start Making Sense: Cognitive and affective confidence measures for explanation generation using epistemic planning

假设有一位游客徒步游览了一座城市。在到达一个几乎可以看到他们回到起点的地方后，他们的导游指着一座大山说：“让我们爬上那座山”。“从那里我们可以很好地看到这座城市。”然而，在看到游客疲惫的表情后，导游补充道：“或者我们可以在那边的酒吧停下来休息一下。”这个场景有两个重要特征：(1)人们喜欢知道发生了什么。在可能没有预料到或预期不到决定的情况下，这一点尤其正确。在这里，可能需要作出解释，不仅是为了证明这一决定是合理的，而且也是为了建立对这一选择的信心--换句话说，要相信这一决定；(2)在对可能采取的行动进行排序时，能够了解情况并适应当前的需要是重要的。在这里，可能需要动态地做出决定。这两个功能加在一起构成了动态信任维护，是预计在不久的将来部署的广泛人工智能系统所需的，例如自动化车辆、服务机器人或基于语音的交互式助手。该项目通过将涉及合作联合行动的认知科学实验研究与实际构建的AI规划工具结合起来应用于解释生成任务，解决了动态信任维护的需求(这在交互和自主系统中通常不存在)。这一挑战将通过以下具体目标来解决：(1)研究人类的合作联合行动，以确定对成功的人类交流目标至关重要的情感、情感或认知因素，(2)利用来自认知科学研究的启发式方法来增强认知规划技术，以及(3)部署所产生的系统来生成与人类相似的解释，并评估所产生的方法与人类参与者的有效性。

Consider a tourist on a walking tour of a city. After reaching a place where they can see they are almost back to the starting point, their tour guide says "Let's go up that hill", pointing to a large hill. "We can get a good view of the city from there." However, on seeing the tired expression on the tourist's face, the guide adds "Or we can stop in that pub over there and take a break." This scenario has two important features: (1) people like to know what is going on. This is especially true in situations where a decision may not have been anticipated or expected. Here, an explanation may be needed not just to justify the decision but also to establish confidence in that choice-in other words, to trust it; (2) being able to read the situation and adapt to the needs of the moment is important when ranking the possible actions that could be taken. Here, a decision may need to be made dynamically. These two features add up to 'dynamic trust maintenance', and are needed for a broad range of the AI systems that are expected to be deployed in the near future, e.g., automated vehicles, service robots, or interactive voice-based assistants.This project addresses the need for dynamic trust maintenance (which is not generally available in interactive and autonomous systems) by bringing together experimental research in cognitive science involving cooperative joint action with the practical construction of AI planning tools to apply to the task of explanation generation. This challenge will be addressed through these concrete objectives: (1) to study cooperative joint action in humans to identify the emotional, affective, or cognitive factors that are essential for successful human communicative goals, (2) to enhance epistemic planning techniques with heuristics derived from the cognitive science studies, and (3) to deploy the resulting system to generate human-like explanations and evaluate the effectiveness of the resulting approach with human participants.