用知识+数据混合模型，让机器人能解释决策，帮人类做关键判断。
Explaining Explaining
- 用知识库加机器学习数据构建可解释的智能体
- 在搜救任务中实现人类能理解的决策说明
- 适合需要高可信度解释的医疗、军事等场景
解释能力对高风险人工智能系统的人信任至关重要。然而，当前几乎全部基于机器学习的AI系统因是黑箱而无法解释。可解释人工智能（XAI）运动通过重新定义“解释”来应对这一问题，而以人为中心的可解释人工智能（HCXAI）虽关注用户需求，却受限于机器学习框架，难以满足真实场景中的解释要求。为实现关键领域用户所需的解释，必须重新思考AI设计路径。本文提出一种混合方法：构建以知识为基础的智能体架构，并在适用时融入机器学习获取的数据。这些智能体将作为人类助手，协助承担人机团队最终决策责任的人员。我们通过一个模拟机器人协作执行搜索任务的演示系统，展示了此类智能体的解释潜力，特别是在系统内部组件的透明展示方面。
原文摘要 · Abstract (English)
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.
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