Recent endeavors towards directly using large language models (LLMs) as agent
models to execute interactive planning tasks have shown commendable results.
Despite their achievements, however, they still struggle with brainless
trial-and-error in global planning and generating hallucinatory actions in
local planning due to their poor understanding of the ''real'' physical world.
Imitating humans' mental world knowledge model which provides global prior
knowledge before the task and maintains local dynamic knowledge during the
task, in this paper, we introduce parametric World Knowledge Model (WKM) to
facilitate agent planning. Concretely, we steer the agent model to
self-synthesize knowledge from both expert and sampled trajectories. Then we
develop WKM, providing prior task knowledge to guide the global planning and
dynamic state knowledge to assist the local planning. Experimental results on
three complex real-world simulated datasets with three state-of-the-art
open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our
method can achieve superior performance compared to various strong baselines.
Besides, we analyze to illustrate that our WKM can effectively alleviate the
blind trial-and-error and hallucinatory action issues, providing strong support
for the agent's understanding of the world. Other interesting findings include:
1) our instance-level task knowledge can generalize better to unseen tasks, 2)
weak WKM can guide strong agent model planning, and 3) unified WKM training has
promising potential for further development. Code will be available at
this https URL

使用大型语言模型作为代理模型来执行交互式规划任务已取得令人称赞的结果。本文引入了参数化的世界知识模型（WKM）来促进代理规划，通过从专家和采样轨迹中自我合成知识，并提供全局任务知识和动态状态知识来指导全局规划和辅助局部规划。实验证明我们的方法在复杂的真实世界模拟数据集上相比强基线能够取得优越性能，减轻了盲目试错和幻觉行为问题，为代理对世界的理解提供了有力支持，同时表明了 WKM 具有更好的泛化性能、弱 WKM 能指导强代理模型的规划，并且统一的 WKM 训练具有进一步发展的潜力。