Knowledge engineering is a discipline that focuses on the creation and
maintenance of processes that generate and apply knowledge. Traditionally,
knowledge engineering approaches have focused on knowledge expressed in formal
languages. The emergence of large language models and their capabilities to
effectively work with natural language, in its broadest sense, raises questions
about the foundations and practice of knowledge engineering. Here, we outline
the potential role of LLMs in knowledge engineering, identifying two central
directions: 1) creating hybrid neuro-symbolic knowledge systems; and 2)
enabling knowledge engineering in natural language. Additionally, we formulate
key open research questions to tackle these directions.

知识工程学是一个专注于生成和应用知识的学科，传统上它主要关注以形式语言表达的知识。然而，大语言模型的出现及其与自然语言的高效处理能力，对知识工程学的基础和实践提出了问题。在这篇论文中，我们提出大语言模型在知识工程学中的潜在作用，并确定两个中心方向：1）创建混合的神经符号知识系统；2）实现自然语言下的知识工程。此外，我们还提出了一些关键的开放性研究问题来解决这些方向。

利用大型语言模型进行知识工程

Knowledge Engineering using Large Language Models

Generative Artificial Intelligence (AI) is one of the most exciting
developments in Computer Science of the last decade. At the same time,
Reinforcement Learning (RL) has emerged as a very successful paradigm for a
variety of machine learning tasks. In this survey, we discuss the state of the
art, opportunities and open research questions in applying RL to generative AI.
In particular, we will discuss three types of applications, namely, RL as an
alternative way for generation without specified objectives; as a way for
generating outputs while concurrently maximizing an objective function; and,
finally, as a way of embedding desired characteristics, which cannot be easily
captured by means of an objective function, into the generative process. We
conclude the survey with an in-depth discussion of the opportunities and
challenges in this fascinating emerging area.

通过应用强化学习于生成型人工智能，我们讨论了现有技术水平、机会以及开放性研究问题。具体而言，我们分析了三种应用情况，包括没有指定目标的生成方法、在最大化目标函数的同时生成输出的方法，以及将难以通过目标函数捕捉到的所需特征嵌入生成过程的方法。这一令人着迷的新兴领域的机会和挑战也在调查中得到了深入讨论。

生成型人工智能的强化学习：现状和机遇，开放研究挑战

Reinforcement Learning for Generative AI: State of the Art,  Opportunities and Open Research Challenges

Team-based invasion sports such as football, basketball and hockey are
similar in the sense that the players are able to move freely around the
playing area; and that player and team performance cannot be fully analysed
without considering the movements and interactions of all players as a group.
State of the art object tracking systems now produce spatio-temporal traces of
player trajectories with high definition and high frequency, and this, in turn,
has facilitated a variety of research efforts, across many disciplines, to
extract insight from the trajectories. We survey recent research efforts that
use spatio-temporal data from team sports as input, and involve non-trivial
computation. This article categorises the research efforts in a coherent
framework and identifies a number of open research questions.

该研究论文调查了利用运动队伍运动轨迹等 spatio-temporal data 的聚类研究，并且提出了一些开放性的问题。