PROC2PDDL: Open-Domain Planning Representations from Texts

• URL: http://arxiv.org/abs/2403.00092v2
• Date: Tue, 2 Jul 2024 04:50:36 GMT
• Title: PROC2PDDL: Open-Domain Planning Representations from Texts
• Authors: Tianyi Zhang, Li Zhang, Zhaoyi Hou, Ziyu Wang, Yuling Gu, Peter Clark, Chris Callison-Burch, Niket Tandon,
• Abstract summary: Proc2PDDL is the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%.
• Score: 56.627183903841164
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Planning in a text-based environment continues to be a major challenge for AI systems. Recent approaches have used language models to predict a planning domain definition (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL , the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate state-of-the-art models on defining the preconditions and effects of actions. We show that Proc2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%. Our analysis shows both syntactic and semantic errors, indicating LMs' deficiency in both generating domain-specific prgorams and reasoning about events. We hope this analysis and dataset helps future progress towards integrating the best of LMs and formal planning.

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