Communication via natural language is a crucial aspect of intelligence, and
it requires computational models to learn and reason about world concepts, with
varying levels of supervision. While there has been significant progress made
on fully-supervised non-interactive tasks, such as question-answering and
procedural text understanding, much of the community has turned to various
sequential interactive tasks, as in semi-Markov text-based games, which have
revealed limitations of existing approaches in terms of coherence, contextual
awareness, and their ability to learn effectively from the environment. In this
paper, we propose a framework for enabling improved functional grounding of
agents in text-based games. Specifically, we consider two forms of domain
knowledge that we inject into learning-based agents: memory of previous correct
actions and affordances of relevant objects in the environment. Our framework
supports three representative model classes: `pure' reinforcement learning (RL)
agents, RL agents enhanced with knowledge graphs, and agents equipped with
language models. Furthermore, we devise multiple injection strategies for the
above domain knowledge types and agent architectures, including injection via
knowledge graphs and augmentation of the existing input encoding strategies. We
perform all experiments on the ScienceWorld text-based game environment, to
illustrate the performance of various model configurations in challenging
science-related instruction-following tasks. Our findings provide crucial
insights on the development of effective natural language processing systems
for interactive contexts.

该文提出了一种通过注入领域知识的方式来改善基于文本游戏中的智能代理的实现过程，并考虑了多种不同的注入策略，包括知识图谱和输入编码策略的增强，实验结果在 ScienceWorld 文本游戏环境中得到证明。