让智能体回答‘为何做X而非F’，解释更短且更易建立信任。
Contrastive explanations of BDI agents
- 扩展BDI智能体，支持回答对比性问题
- 对比解释比普通解释长度减少显著
- 用户更倾向对比解释，有助于提升信任感
自主系统提供解释的能力对透明性及信任建立至关重要。已有研究定义了信念-欲望-意图（BDI）智能体回答“为何执行动作X”问题的机制，但人们常问的是对比性问题：“为何做X而非F？”本文扩展该机制以支持此类提问。计算评估显示，使用对比性问题可显著缩短解释长度。人类实验表明，对比性回答更受青睐，并在提升信任、理解度和对系统正确性的信心方面有一定效果。此外，我们评估了是否需要解释本身：出人意料的是，在某些情况下，完整解释甚至比不解释更差。
原文摘要 · Abstract (English)
The ability of autonomous systems to provide explanations is important for supporting transparency and aiding the development of (appropriate) trust. Prior work has defined a mechanism for Belief-Desire-Intention (BDI) agents to be able to answer questions of the form ``why did you do action $X$?''. However, we know that we ask \emph{contrastive} questions (``why did you do $X$ \emph{instead of} $F$?''). We therefore extend previous work to be able to answer such questions. A computational evaluation shows that using contrastive questions yields a significant reduction in explanation length. A human subject evaluation was conducted to assess whether such contrastive answers are preferred, and how well they support trust development and transparency. We found some evidence for contrastive answers being preferred, and some evidence that they led to higher trust, perceived understanding, and confidence in the system's correctness. We also evaluated the benefit of providing explanations at all. Surprisingly, there was not a clear benefit, and in some situations we found evidence that providing a (full) explanation was worse than not providing any explanation.
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