Building world models is essential for planning in real-world domains such as businesses. Since such domains have rich semantics, we can leverage world knowledge to effectively model complex action effects and causal relationships from limited data. In this work, we propose CASSANDRA, a neurosymbolic world modeling approach that leverages an LLM as a knowledge prior to construct lightweight transition models for planning. CASSANDRA integrates two components: (1) LLM-synthesized code to model deterministic features, and (2) LLM-guided structure learning of a probabilistic graphical model to capture causal relationships among stochastic variables. We evaluate CASSANDRA in (i) a small-scale coffee-shop simulator and (ii) a complex theme park business simulator, where we demonstrate significant improvements in transition prediction and planning over baselines.

翻译：构建世界模型对于商业等现实领域中的规划至关重要。由于此类领域具有丰富的语义，我们可以利用世界知识，从有限数据中有效建模复杂的行动效果与因果关系。在本工作中，我们提出CASSANDRA，一种神经符号化世界建模方法，它利用大型语言模型（LLM）作为知识先验来构建用于规划的轻量级转移模型。CASSANDRA整合了两个组件：（1）LLM合成的代码用于建模确定性特征；（2）LLM引导的概率图模型结构学习，用于捕捉随机变量间的因果关系。我们在（i）一个小型咖啡店模拟器和（ii）一个复杂的主题公园商业模拟器中评估CASSANDRA，结果表明其在转移预测和规划方面相较于基线方法有显著提升。