We explore how interaction with large language models (LLMs) can give rise to
emergent behaviors, empowering players to participate in the evolution of game
narratives. Our testbed is a text-adventure game in which players attempt to
solve a mystery under a fixed narrative premise, but can freely interact with
non-player characters generated by GPT-4, a large language model. We recruit 28
gamers to play the game and use GPT-4 to automatically convert the game logs
into a node-graph representing the narrative in the player's gameplay. We find
that through their interactions with the non-deterministic behavior of the LLM,
players are able to discover interesting new emergent nodes that were not a
part of the original narrative but have potential for being fun and engaging.
Players that created the most emergent nodes tended to be those that often
enjoy games that facilitate discovery, exploration and experimentation.

通过与大型语言模型的互动，玩家能够参与游戏叙事的演化，发现有趣且具有吸引力的新节点，进而创造出非原始叙事中不存在的内容。

LLM 驱动的游戏叙事中的玩家驱动新生

Player-Driven Emergence in LLM-Driven Game Narrative

One major challenge in reinforcement learning (RL) is the large amount of
steps for the RL agent needs to converge in the training process and learn the
optimal policy, especially in text-based game environments where the action
space is extensive. However, non-player characters (NPCs) sometimes hold some
key information about the game, which can potentially help to train RL agents
faster. Thus, this paper explores how to interact and converse with NPC agents
to get the key information using large language models (LLMs), as well as
incorporate this information to speed up RL agent's training using knowledge
graphs (KGs) and Story Shaping.

使用大型语言模型与知识图谱相结合，通过与非玩家角色的互动交流获取关键信息，以加快强化学习代理的训练。

对话塑造：通过 NPC 互动赋能代理

Dialogue Shaping: Empowering Agents through NPC Interaction

There is a high demand for high-quality Non-Player Characters (NPCs) in video
games. Hand-crafting their behavior is a labor intensive and error prone
engineering process with limited controls exposed to the game designers. We
propose to create such NPC behaviors interactively by training an agent in the
target environment using imitation learning with a human in the loop. While
traditional behavior cloning may fall short of achieving the desired
performance, we show that interactivity can substantially improve it with a
modest amount of human efforts. The model we train is a multi-resolution
ensemble of Markov models, which can be used as is or can be further
"compressed" into a more compact model for inference on consumer devices. We
illustrate our approach on an example in OpenAI Gym, where a human can help to
quickly train an agent with only a handful of interactive demonstrations. We
also outline our experiments with NPC training for a first-person shooter game
currently in development.

通过训练一个多分辨率的马尔可夫模型的集合，我们提出了一种互动式的模仿学习的方法来创建高质量的 NPC 行为，从而大大减少了传统手工制作 NPC 的时间和工作量。