Imitation learning is an approach in which an agent learns how to execute a
task by trying to mimic how one or more teachers perform it. This learning
approach offers a compromise between the time it takes to learn a new task and
the effort needed to collect teacher samples for the agent. It achieves this by
balancing learning from the teacher, who has some information on how to perform
the task, and deviating from their examples when necessary, such as states not
present in the teacher samples. Consequently, the field of imitation learning
has received much attention from researchers in recent years, resulting in many
new methods and applications. However, with this increase in published work and
past surveys focusing mainly on methodology, a lack of standardisation became
more prominent in the field. This non-standardisation is evident in the use of
environments, which appear in no more than two works, and evaluation processes,
such as qualitative analysis, that have become rare in current literature. In
this survey, we systematically review current imitation learning literature and
present our findings by (i) classifying imitation learning techniques,
environments and metrics by introducing novel taxonomies; (ii) reflecting on
main problems from the literature; and (iii) presenting challenges and future
directions for researchers.

模仿学习的文献内容进行了系统回顾，通过引入新的分类法对模仿学习技术、环境和度量进行了分类，反思了文献中的主要问题，并提出了研究者面临的挑战和未来发展方向。

模仿学习：学习方法、环境和评估方法综述

Imitation Learning: A Survey of Learning Methods, Environments and  Metrics

In the European context, both the EU AI Act proposal and the draft
Standardisation Request on safe and trustworthy AI link standardisation to
fundamental rights. However, these texts do not provide any guidelines that
specify and detail the relationship between AI standards and fundamental
rights, its meaning or implication. This chapter aims to clarify this critical
regulatory blind spot. The main issue tackled is whether the adoption of AI
harmonised standards, based on the future AI Act, should take into account
fundamental rights. In our view, the response is yes. The high risks posed by
certain AI systems relate in particular to infringements of fundamental rights.
Therefore, mitigating such risks involves fundamental rights considerations and
this is what future harmonised standards should reflect. At the same time,
valid criticisms of the European standardisation process have to be addressed.
Finally, the practical incorporation of fundamental rights considerations in
the ongoing European standardisation of AI systems is discussed.

欧洲背景下，EU AI 法案提案和 AI 安全可信性标准化申请草案将标准化与基本权利联系起来，但没有提供明确规范关于 AI 标准与基本权利之间的关系、含义或意义。本章旨在澄清这一关键的监管空白，探讨采用基于未来 AI 法案的 AI 标准是否应考虑基本权利，回答是肯定的。某些 AI 系统所带来的高风险特别涉及对基本权利的侵犯，因此减轻此类风险涉及到基本权利的考虑，而未来的标准化应该反映这一点。同时，还需要解决对欧洲标准化进程的有效批评。最后，讨论如何在正在进行的欧洲 AI 系统标准化中实际整合基本权利的考虑。

在欧洲人工智能标准化中考虑基本权利：荒唐还是战略联盟？

Considering Fundamental Rights in the European Standardisation of  Artificial Intelligence: Nonsense or Strategic Alliance?

While research on explainable AI (XAI) is booming and explanation techniques
have proven promising in many application domains, standardised human-centred
evaluation procedures are still missing. In addition, current evaluation
procedures do not assess XAI methods holistically in the sense that they do not
treat explanations' effects on humans as a complex user experience. To tackle
this challenge, we propose to adapt the User-Centric Evaluation Framework used
in recommender systems: we integrate explanation aspects, summarise explanation
properties, indicate relations between them, and categorise metrics that
measure these properties. With this comprehensive evaluation framework, we hope
to contribute to the human-centred standardisation of XAI evaluation.

通过在解释性人工智能（XAI）中引入用户中心评估框架，我们希望综合评估方法，总结解释属性，建立相互关系，并分类度量这些属性，以期为 XAI 评估的人本标准化做出贡献。