We investigated the potential and limitations of generative artificial
intelligence (AI) in reflecting the authors' cognitive processes through
creative expression. The focus is on the AI-generated artwork's ability to
understand human intent (alignment) and visually represent emotions based on
criteria such as creativity, aesthetic, novelty, amusement, and depth. Results
show a preference for images based on the descriptions of the authors' emotions
over the main events. We also found that images that overrepresent specific
elements or stereotypes negatively impact AI alignment. Our findings suggest
that AI could facilitate creativity and the self-expression of emotions. Our
research framework with generative AIs can help design AI-based interventions
in related fields (e.g., mental health education, therapy, and counseling).

该研究探讨了生成人工智能在反映作者认知过程、进行创意表达方面的潜力和限制，发现根据创意、美学、新颖性、娱乐性和深度等标准，AI 生成的艺术作品能够理解人类意图和基于情感进行视觉表现，并发现过度表现某些元素或刻板印象的图像对于 AI 的对齐性有负面影响。研究结果表明，AI 在促进创造力和自我情感表达方面具有潜力，而采用生成 AI 的框架可以帮助设计相关领域的人工智能干预措施（例如心理健康教育、治疗和咨询）。

情感画像：通过 AI 生成艺术作品赋予自我表达能力

A Portrait of Emotion: Empowering Self-Expression through AI-Generated  Art

Inferring intent from observed behavior has been studied extensively within
the frameworks of Bayesian inverse planning and inverse reinforcement learning.
These methods infer a goal or reward function that best explains the actions of
the observed agent, typically a human demonstrator. Another agent can use this
inferred intent to predict, imitate, or assist the human user. However, a
central assumption in inverse reinforcement learning is that the demonstrator
is close to optimal. While models of suboptimal behavior exist, they typically
assume that suboptimal actions are the result of some type of random noise or a
known cognitive bias, like temporal inconsistency. In this paper, we take an
alternative approach, and model suboptimal behavior as the result of internal
model misspecification: the reason that user actions might deviate from
near-optimal actions is that the user has an incorrect set of beliefs about the
rules -- the dynamics -- governing how actions affect the environment. Our
insight is that while demonstrated actions may be suboptimal in the real world,
they may actually be near-optimal with respect to the user's internal model of
the dynamics. By estimating these internal beliefs from observed behavior, we
arrive at a new method for inferring intent. We demonstrate in simulation and
in a user study with 12 participants that this approach enables us to more
accurately model human intent, and can be used in a variety of applications,
including offering assistance in a shared autonomy framework and inferring
human preferences.

本文提出一种基于内部信念的模型，推断人类意图以更准确的模拟人类，并可以应用于共享自治框架和推断人类偏好等应用中。