Multi-agent influence diagrams (MAIDs) are a popular form of graphical model
that, for certain classes of games, have been shown to offer key complexity and
explainability advantages over traditional extensive form game (EFG)
representations. In this paper, we extend previous work on MAIDs by introducing
the concept of a MAID subgame, as well as subgame perfect and trembling hand
perfect equilibrium refinements. We then prove several equivalence results
between MAIDs and EFGs. Finally, we describe an open source implementation for
reasoning about MAIDs and computing their equilibria.

本文介绍了多智能体影响图的基础概念，引入了子博弈的概念和博弈均衡。同时，证明了多智能体影响图和全局博弈图之间的等价性，并描述了一种开源的实现方法。

多智能体影响图的均衡优化：理论与实践

Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and  Practice

In recent years, there are a large number of recommendation algorithms
proposed in the literature, from traditional collaborative filtering to deep
learning algorithms. However, the concerns about how to standardize open source
implementation of recommendation algorithms continually increase in the
research community. In the light of this challenge, we propose a unified,
comprehensive and efficient recommender system library called RecBole, which
provides a unified framework to develop and reproduce recommendation algorithms
for research purpose. In this library, we implement 73 recommendation models on
28 benchmark datasets, covering the categories of general recommendation,
sequential recommendation, context-aware recommendation and knowledge-based
recommendation. We implement the RecBole library based on PyTorch, which is one
of the most popular deep learning frameworks. Our library is featured in many
aspects, including general and extensible data structures, comprehensive
benchmark models and datasets, efficient GPU-accelerated execution, and
extensive and standard evaluation protocols. We provide a series of auxiliary
functions, tools, and scripts to facilitate the use of this library, such as
automatic parameter tuning and break-point resume. Such a framework is useful
to standardize the implementation and evaluation of recommender systems. The
project and documents are released at this https URL

RecBole 是一个统一、综合、高效的推荐系统库，提供了通用推荐、序列推荐、上下文感知推荐和基于知识的推荐等 73 个模型在 28 个基准数据集上的实现，并具有广泛的 GPU 加速执行和标准化评估协议，适用于推荐系统的标准化实现和评估。