Co-creativity in music refers to two or more musicians or musical agents
interacting with one another by composing or improvising music. However, this
is a very subjective process and each musician has their own preference as to
which improvisation is better for some context. In this paper, we aim to create
a measure based on total information flow to quantitatively evaluate the
co-creativity process in music. In other words, our measure is an indication of
how "good" a creative musical process is. Our main hypothesis is that a good
musical creation would maximize information flow between the participants
captured by music voices recorded in separate tracks. We propose a method to
compute the information flow using pre-trained generative models as entropy
estimators. We demonstrate how our method matches with human perception using a
qualitative study.

通过量化评估音乐合创过程中的信息流，本文旨在创造一种基于总信息流的度量，即创作音乐过程的 “好坏” 指示。通过使用预训练的生成模型作为熵估计器计算信息流的方法，我们展示了如何通过定性研究验证了我们的方法与人类感知的匹配性。

评估合创性使用总体信息流

Evaluating Co-Creativity using Total Information Flow

The term co-creativity has been used to describe a wide variety of human-AI
assemblages in which human and AI are both involved in a creative endeavor. In
order to assist with disambiguating research efforts, we present an ontology of
co-creative systems, focusing on how responsibilities are divided between human
and AI system and the information exchanged between them. We extend Lubart's
original ontology of creativity support tools with three new categories
emphasizing artificial intelligence: computer-as-subcontractor,
computer-as-critic, and computer-as-teammate, some of which have
sub-categorizations.

人工智能在协同创造中的角色和责任分配是本研究的关键，主要通过建立一个包含人工智能的合作系统本体论来澄清相关研究，其中包括计算机作为分包商、计算机作为评论者、计算机作为队友等不同类别的分类。

协同创造人工智能系统的本体论

An Ontology of Co-Creative AI Systems

Generative Artificial Intelligence systems have been developed for image,
code, story, and game generation with the goal of facilitating human
creativity. Recent work on neural generative systems has emphasized one
particular means of interacting with AI systems: the user provides a
specification, usually in the form of prompts, and the AI system generates the
content. However, there are other configurations of human and AI coordination,
such as co-creativity (CC) in which both human and AI systems can contribute to
content creation, and mixed-initiative (MI) in which both human and AI systems
can initiate content changes. In this paper, we define a hypothetical human-AI
configuration design space consisting of different means for humans and AI
systems to communicate creative intent to each other. We conduct a human
participant study with 185 participants to understand how users want to
interact with differently configured MI-CC systems. We find out that MI-CC
systems with more extensive coverage of the design space are rated higher or on
par on a variety of creative and goal-completion metrics, demonstrating that
wider coverage of the design space can improve user experience and achievement
when using the system; Preference varies greatly between expertise groups,
suggesting the development of adaptive, personalized MI-CC systems;
Participants identified new design space dimensions including scrutability --
the ability to poke and prod at models -- and explainability.

本研究探讨了人工智能生成系统的不同工作模式，包括人机协作生成，以及探究人们对于这些模式的偏好和使用体验。我们的实验表明，覆盖更广阔的设计空间可以提高用户的满意度和成就感，而谨慎的介入和解释可以更好的适应不同技能水平的用户。