论文标题
基于代理商设计的任务模型作为问题回答的解释
Explanation as Question Answering based on a Task Model of the Agent's Design
论文作者
论文摘要
我们描述了对AI代理中既以人为本又基于设计的解释产生的立场。我们通过焦点小组通过参与设计来收集有关AI代理商的工作问题。我们通过任务方法知识模型捕获了代理的设计，该模型明确指定了代理的任务和目标，以及它用于完成任务的机制，知识和词汇。我们通过在Skillsync中产生的解释来说明我们的方法，Skillsync是一种AI代理，该代理将公司和学院连接起来，以使工人提高和重新锻炼。特别是，我们在Skillsync中嵌入了一个名为AskJill的提问代理，AskJill包含Skillsync设计的TMK模型。 AskJill目前回答有关Skillsync任务和词汇的人类生成的问题，从而有助于解释其如何产生建议。
We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through participatory design by focus groups. We capture an agent's design through a Task-Method-Knowledge model that explicitly specifies the agent's tasks and goals, as well as the mechanisms, knowledge and vocabulary it uses for accomplishing the tasks. We illustrate our approach through the generation of explanations in Skillsync, an AI agent that links companies and colleges for worker upskilling and reskilling. In particular, we embed a question-answering agent called AskJill in Skillsync, where AskJill contains a TMK model of Skillsync's design. AskJill presently answers human-generated questions about Skillsync's tasks and vocabulary, and thereby helps explain how it produces its recommendations.
