Imaginative play is an area of creativity that could allow robots to engage
with the world around them in a much more personified way. Imaginary play can
be seen as taking real objects and locations and using them as imaginary
objects and locations in virtual scenarios. We adopted the story generation
capability of large language models (LLMs) to obtain the stories used for
imaginary play with human-written prompts. Those generated stories will be
simplified and mapped into action sequences that can guide the agent in
imaginary play. To evaluate whether the agent can successfully finish the
imaginary play, we also designed a text adventure game to simulate a house as
the playground for the agent to interact.

通过采用大型语言模型的故事生成能力，我们将生成的故事简化并映射为行动序列，以引导智能体进行想象性游戏，并设计了一个文本冒险游戏来模拟智能体在房屋中进行互动，以评估其是否能成功完成想象性游戏。

环境探险：教授 ChatGPT 开发复杂故事

Ambient Adventures: Teaching ChatGPT on Developing Complex Stories

While language models have become more capable of producing compelling
language, we find there are still gaps in maintaining consistency, especially
when describing events in a dynamically changing world. We study the setting of
generating narratives in an open world text adventure game, where a graph
representation of the underlying game state can be used to train models that
consume and output both grounded graph representations and natural language
descriptions and actions. We build a large set of tasks by combining
crowdsourced and simulated gameplays with a novel dataset of complex actions in
order to to construct such models. We find it is possible to improve the
consistency of action narration models by training on graph contexts and
targets, even if graphs are not present at test time. This is shown both in
automatic metrics and human evaluations. We plan to release our code, the new
set of tasks, and best performing models.

本文研究在一个开放式世界的文本冒险游戏中生成叙事的设定，使用游戏状态的图形表示来训练模型，可以消耗和输出基于图形的表示和自然语言描述和动作。通过结合众包和模拟游戏玩法构建一组大量的任务和复杂的动作数据集来构建这样的模型，发现通过在图形上下文和目标上训练可以改善动作叙述模型的一致性，即使在测试时没有图形。这在自动指标和人类评估中都得到了证明。我们计划公开代码、新一组任务和最佳表现模型。

将常识世界模型注入图谱知识

Infusing Commonsense World Models with Graph Knowledge

We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully.

本文介绍了一个大规模的众包文字冒险游戏作为研究平台，用于研究基于场景的对话。在其中，代理可以感知，表情和行动，同时与其他代理进行对话。我们介绍了在这种情况下训练最先进的生成模型和检索模型的结果。我们展示了除了过去的对话，这些模型还能够有效地使用潜在世界的状态来调整它们的预测，特别是使用本地环境的详细信息，包括位置描述和其中存在的对象（及其可用性）和角色（及其先前的行动），可以更好地预测代理行为和对话。我们分析了在这种设置中成功落地所需的要素，以及每个因素如何与能够成功说话和行动的代理相关。