Counterfactual Explanation (CE) techniques have garnered attention as a means
to provide insights to the users engaging with AI systems. While extensively
researched in domains such as medical imaging and autonomous vehicles, Graph
Counterfactual Explanation (GCE) methods have been comparatively
under-explored. GCEs generate a new graph similar to the original one, with a
different outcome grounded on the underlying predictive model. Among these GCE
techniques, those rooted in generative mechanisms have received relatively
limited investigation despite demonstrating impressive accomplishments in other
domains, such as artistic styles and natural language modelling. The preference
for generative explainers stems from their capacity to generate counterfactual
instances during inference, leveraging autonomously acquired perturbations of
the input graph. Motivated by the rationales above, our study introduces
RSGG-CE, a novel Robust Stochastic Graph Generator for Counterfactual
Explanations able to produce counterfactual examples from the learned latent
space considering a partially ordered generation sequence. Furthermore, we
undertake quantitative and qualitative analyses to compare RSGG-CE's
performance against SoA generative explainers, highlighting its increased
ability to engendering plausible counterfactual candidates.

本研究介绍了一种新的稳健随机图生成器 RSGG-CE，能够从学习的潜在空间产生可行的反事实例子，并通过定序生成序列考虑来量化和质化地比较 RSGG-CE 与现有方法生成反事实候选者的性能，凸显其更高的能力。

鲁棒的随机图生成器用于反事实解释

Robust Stochastic Graph Generator for Counterfactual Explanations

We introduce TextWorld, a sandbox learning environment for the training and
evaluation of RL agents on text-based games. TextWorld is a Python library that
handles interactive play-through of text games, as well as backend functions
like state tracking and reward assignment. It comes with a curated list of
games whose features and challenges we have analyzed. More significantly, it
enables users to handcraft or automatically generate new games. Its generative
mechanisms give precise control over the difficulty, scope, and language of
constructed games, and can be used to relax challenges inherent to commercial
text games like partial observability and sparse rewards. By generating sets of
varied but similar games, TextWorld can also be used to study generalization
and transfer learning. We cast text-based games in the Reinforcement Learning
formalism, use our framework to develop a set of benchmark games, and evaluate
several baseline agents on this set and the curated list.

TextWorld 是为训练和评估基于强化学习的 RL agents 在基于文本的游戏上而设计的一个 Python 库，该文献使用 TextWorld 构建基于强化学习形式主义的基准游戏，并评估了几个基线代理。