Rule-based adaptation is a foundational approach to self-adaptation,
characterized by its human readability and rapid response. However, building
high-performance and robust adaptation rules is often a challenge because it
essentially involves searching the optimal design in a complex (variables)
space. In response, this paper attempt to employ large language models (LLMs)
as a optimizer to construct and optimize adaptation rules, leveraging the
common sense and reasoning capabilities inherent in LLMs. Preliminary
experiments conducted in SWIM have validated the effectiveness and limitation
of our method.

采用大型语言模型作为优化器构建和优化自适应规则的方法经过 SWIM 中的初步实验验证了其有效性和局限性。

大型语言模型自动适应规则优化

Automatic Adaptation Rule Optimization via Large Language Models

Interaction is a fundamental part of using any computer system but it is
still an issue for people with special needs. In order to improve this
situation, this paper describes a new device-interaction model based on
adaptation rules for user models. The aim is the adaptation at the interaction
level, taking into account the interaction device features in order to improve
the usability through the user experience in the education sector. In the
evaluation process, several students from a special education center have
participated. These students have either a physical or sensory disability or
autism. The results are promising enough to consider that this model will be
able to help students with disabilities to interact with a computer system
which will inevitably provide tremendous benefits to their academic and
personal development.

本文提出了一种新的以适应规则为基础的设备交互模型，旨在适应交互级别，通过用户体验以提高教育领域的可用性，评估结果对于帮助残疾学生与计算机交互有着巨大的潜在益处。

面向特殊需求用户的设备交互模型

A device-interaction model for users with special needs

The case difference heuristic (CDH) approach is a knowledge-light method for
learning case adaptation knowledge from the case base of a case-based reasoning
system. Given a pair of cases, the CDH approach attributes the difference in
their solutions to the difference in the problems they solve, and generates
adaptation rules to adjust solutions accordingly when a retrieved case and new
query have similar problem differences. As an alternative to learning
adaptation rules, several researchers have applied neural networks to learn to
predict solution differences from problem differences. Previous work on such
approaches has assumed that the feature set describing problems is predefined.
This paper investigates a two-phase process combining deep learning for feature
extraction and neural network based adaptation learning from extracted
features. Its performance is demonstrated in a regression task on an image
data: predicting age given the image of a face. Results show that the combined
process can successfully learn adaptation knowledge applicable to nonsymbolic
differences in cases. The CBR system achieves slightly lower performance
overall than a baseline deep network regressor, but better performance than the
baseline on novel queries.

本研究探讨了深度学习与神经网络相结合的方法来学习适应性规则，并成功地将其应用于非符号差异的案例，并在图像数据的回归任务中表现出优异的性能，既能比基础深度网络回归器获得更优的性能，又能比基础方法在新查询方面获得更好的性能。