CASSANDRA: 確率的ワールドモデリングのためのプログラム的・確率論的学習と推論
CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modeling
LLMを知識事前分布として活用し、決定論的特徴をコードで、確率的因果関係を確率グラフィカルモデルでモデル化するニューロシンボリックなワールドモデルを提案し、計画精度を向上させた。
著者: Panagiotis Lymperopoulos, Abhiramon Rajasekharan, Ian Berlot-Attwell, Stéphane Aroca-Ouellette, Kaheer Suleman
分類: cs.LG
原文アブストラクト
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.