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

考虑一个游客在一个城市的步行之旅。到达一个地方，他们可以看到他们几乎回到了起点，他们的导游说：“让我们去那座山”，指着一座大山。“我们可以从那里看到这个城市的美景。然而，看到游客脸上疲惫的表情，导游补充道：“或者我们可以在那边的酒吧停下来休息一下。”“这种情况有两个重要的特点：（1）人们喜欢知道正在发生的事情，尤其是在没有预料到或没有预料到某个决定的情况下。在这里，可能需要一个解释，不仅是为了证明这个决定，而且也是为了建立对这个选择的信心，换句话说，信任它;（2）能够阅读情况并适应当时的需要，这在对可能采取的行动进行排序时很重要。这里，可能需要动态地做出决定。这两个功能加起来就是“动态信任维护”，并且是预计在不久的将来部署的广泛AI系统所需要的，例如，自动化车辆、服务机器人或交互式语音助理。本项目通过将涉及合作联合行动的认知科学实验研究与应用于解释生成任务的AI规划工具的实际构建相结合，解决了动态信任维护的需求（这在交互式和自主系统中通常不存在）。将通过以下具体目标应对这一挑战：（1）研究人类的合作联合行动，以确定成功的人类交际目标所必需的情感、情感或认知因素，（2）用来自认知科学研究的知识来增强认知规划技术，以及（3）部署所得到的系统以生成类似人类的解释并评估所得到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.