Music is one of the Gardner's intelligences in his theory of multiple
intelligences. How humans perceive and understand music is still being studied
and is crucial to develop artificial intelligence models that imitate such
processes. Music generation with Artificial Intelligence is an emerging field
that is gaining much attention in the recent years. In this paper, we describe
how humans compose music and how new AI systems could imitate such process by
comparing past and recent advances in the field with music composition
techniques. To understand how AI models and algorithms generate music and the
potential applications that might appear in the future, we explore, analyze and
describe the agents that take part of the music generation process: the
datasets, models, interfaces, the users and the generated music. We mention
possible applications that might benefit from this field and we also propose
new trends and future research directions that could be explored in the future.

本文讨论了如何使用 AI 系统来模拟人类音乐创作的过程，同时分析了用于音乐生成的数据集、模型、用户界面以及未来的应用和研究方向。

音乐生成的人工智能调查：代理、领域和视角

A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives

Conventional automatic doors cannot distinguish between people wishing to
pass through the door and people passing by the door, so they often open
unnecessarily. This leads to the need to adopt new systems in both commercial
and non-commercial environments: smart doors. In particular, a smart door
system predicts the intention of people near the door based on the social
context of the surrounding environment and then makes rational decisions about
whether or not to open the door. This work proposes the first position paper
related to smart doors, without bells and whistles. We first point out that the
problem not only concerns reliability, climate control, safety, and mode of
operation. Indeed, a system to predict the intention of people near the door
also involves a deeper understanding of the social context of the scene through
a complex combined analysis of proxemics and scene reasoning. Furthermore, we
conduct an exhaustive literature review about automatic doors, providing a
novel system formulation. Also, we present an analysis of the possible future
application of smart doors, a description of the ethical shortcomings, and
legislative issues.

提出了一篇关于智能门的位置论文，该论文首先指出了问题不仅涉及可靠性、气候控制、安全和操作模式，而且还需要通过对邻近门口人的社会背景和场景推理的复杂组合分析来深入理解社会背景，并对自动门进行了详尽的文献综述和未来应用的分析，以及对伦理和立法问题的描述。

智能门：一篇立场论文

Toward Smart Doors: A Position Paper

The analysis and control of large-population systems is of great interest to
diverse areas of research and engineering, ranging from epidemiology over
robotic swarms to economics and finance. An increasingly popular and effective
approach to realizing sequential decision-making in multi-agent systems is
through multi-agent reinforcement learning, as it allows for an automatic and
model-free analysis of highly complex systems. However, the key issue of
scalability complicates the design of control and reinforcement learning
algorithms particularly in systems with large populations of agents. While
reinforcement learning has found resounding empirical success in many scenarios
with few agents, problems with many agents quickly become intractable and
necessitate special consideration. In this survey, we will shed light on
current approaches to tractably understanding and analyzing large-population
systems, both through multi-agent reinforcement learning and through adjacent
areas of research such as mean-field games, collective intelligence, or complex
network theory. These classically independent subject areas offer a variety of
approaches to understanding or modeling large-population systems, which may be
of great use for the formulation of tractable MARL algorithms in the future.
Finally, we survey potential areas of application for large-scale control and
identify fruitful future applications of learning algorithms in practical
systems. We hope that our survey could provide insight and future directions to
junior and senior researchers in theoretical and applied sciences alike.

这篇综述论文研究了在大规模群体系统中进行可控分析和多智能体强化学习的关键问题和挑战，介绍了现有的相关研究领域和方法，并讨论了未来的应用前景和方向。