This paper outlines the ethical implications of text simplification within
the framework of assistive systems. We argue that a distinction should be made
between the technologies that perform text simplification and the realisation
of these in assistive technologies. When using the latter as a motivation for
research, it is important that the subsequent ethical implications be carefully
considered. We provide guidelines for the framing of text simplification
independently of assistive systems, as well as suggesting directions for future
research and discussion based on the concerns raised.

本文探讨了在辅助系统中进行文本简化的伦理问题，并强调应该区分文本简化技术和在辅助技术中的实现。我们提供了独立于辅助系统的文本简化框架指南，并根据所提出的问题建议未来研究和讨论方向。

论文本简化的伦理考虑

On the Ethical Considerations of Text Simplification

Developing artificial intelligence based assistive systems to aid Persons
with Dementia (PwD) requires large amounts of training data. However, data
collection poses ethical, legal, economic, and logistic issues. Synthetic data
generation tools, in this regard, provide a potential solution. However, we
believe that already available such tools do not adequately reflect cognitive
deficiencies in behavior simulation. To counter these issues we propose a
simulation model (SimDem ) that primarily focuses on cognitive impairments
suffered by PwD and can be easily configured and adapted by the users to model
and evaluate assistive solutions.

利用人工智能辅助系统来帮助患有痴呆症的人需要大量的训练数据，但数据采集带来了伦理、法律、经济和后勤问题。合成数据生成工具提供了潜在的解决方案，但我们认为目前可用的这些工具未能充分反映行为模拟中的认知障碍。因此，我们提出了一个模拟模型（SimDem），重点关注患有痴呆症的认知障碍，用户可以轻松配置和适应模型，以模拟和评估辅助解决方案。

SimDem：用于模拟患有痴呆症及其援助者的多智能体仿真环境

SimDem A Multi-agent Simulation Environment to Model Persons with  Dementia and their Assistance

Automatically reasoning about future human behaviors is a difficult problem
but has significant practical applications to assistive systems. Part of this
difficulty stems from learning systems' inability to represent all kinds of
behaviors. Some behaviors, such as motion, are best described with continuous
representations, whereas others, such as picking up a cup, are best described
with discrete representations. Furthermore, human behavior is generally not
fixed: people can change their habits and routines. This suggests these systems
must be able to learn and adapt continuously. In this work, we develop an
efficient deep generative model to jointly forecast a person's future discrete
actions and continuous motions. On a large-scale egocentric dataset,
EPIC-KITCHENS, we observe our method generates high-quality and diverse samples
while exhibiting better generalization than related generative models. Finally,
we propose a variant to continually learn our model from streaming data,
observe its practical effectiveness, and theoretically justify its learning
efficiency.

本文提出使用高效深度生成模型，预测人类未来的离散和连续行为，进而在实践中实现较好的样本生成效果，并从流数据中逐步学习提高其学习效率。

基于生成混合表示的活动预测与无悔学习

Generative Hybrid Representations for Activity Forecasting with  No-Regret Learning

In shared autonomy, user input is combined with semi-autonomous control to
achieve a common goal. The goal is often unknown ex-ante, so prior work enables
agents to infer the goal from user input and assist with the task. Such methods
tend to assume some combination of knowledge of the dynamics of the
environment, the user's policy given their goal, and the set of possible goals
the user might target, which limits their application to real-world scenarios.
We propose a deep reinforcement learning framework for model-free shared
autonomy that lifts these assumptions. We use human-in-the-loop reinforcement
learning with neural network function approximation to learn an end-to-end
mapping from environmental observation and user input to agent action values,
with task reward as the only form of supervision. This approach poses the
challenge of following user commands closely enough to provide the user with
real-time action feedback and thereby ensure high-quality user input, but also
deviating from the user's actions when they are suboptimal. We balance these
two needs by discarding actions whose values fall below some threshold, then
selecting the remaining action closest to the user's input. Controlled studies
with users (n = 12) and synthetic pilots playing a video game, and a pilot
study with users (n = 4) flying a real quadrotor, demonstrate the ability of
our algorithm to assist users with real-time control tasks in which the agent
cannot directly access the user's private information through observations, but
receives a reward signal and user input that both depend on the user's intent.
The agent learns to assist the user without access to this private information,
implicitly inferring it from the user's input. This paper is a proof of concept
that illustrates the potential for deep reinforcement learning to enable
flexible and practical assistive systems.

论文提出了一种深度强化学习框架，结合人类输入与半自治控制，通过学习用户意图，帮助用户在实时控制任务中实现目标，成果是一种灵活、实用的辅助系统。