PROC2PDDL: Open-Domain Planning Representations from Texts

Core Concepts

The author presents PROC2PDDL, a dataset for evaluating open-domain procedural texts and PDDL representations, highlighting the challenges faced by language models in generating domain-specific programs.

Abstract

PROC2PDDL introduces a dataset pairing procedural texts with PDDL representations to evaluate action modeling. Language models struggle with syntactic and semantic errors in defining actions, emphasizing the need for improved integration of LM and formal planning.

Stats

PROC2PDDL is highly challenging, with GPT-3.5's success rate close to 0% and GPT-4's around 35%.
Using a summarize-extract-translate pipeline improves metrics by 2-3%.
LMs are worse at predicting preconditions than effects, with GPT-4 having 36.7% accuracy for parameters, 31.1% for preconditions, and 53.0% for effects.

Quotes

Key Insights Distilled From

PROC2PDDL

by Tianyi Zhang... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00092.pdf

Deeper Inquiries

How can the limitations of planning languages like PDDL impact real-world applications?

The limitations of planning languages like PDDL can have significant implications for real-world applications. One major limitation is the complexity and expressiveness of PDDL, which may not fully capture all the nuances and intricacies of a real-world environment. This could lead to challenges in accurately modeling complex scenarios, resulting in suboptimal or inefficient plans. Additionally, the rigid syntax and structure of PDDL may make it difficult for non-experts to interact with or modify existing plans easily, limiting its usability in dynamic environments where changes are frequent.
Furthermore, as planning tasks become more sophisticated and require interactions with diverse systems or domains, the constraints imposed by traditional planning languages like PDDL may hinder adaptability and scalability. Real-world applications often involve multiple agents, uncertain environments, and evolving goals, which may not be adequately addressed within the confines of standard planning formalisms. This could result in limited flexibility when dealing with unforeseen circumstances or novel situations.
In essence, these limitations could impede the practical utility of AI systems that rely on PDDL for decision-making processes in dynamic and complex real-world settings.

What alternative approaches could be explored to improve LM performance in text-based planning?

To enhance LM performance in text-based planning tasks such as those presented in PROC2PDDL, several alternative approaches can be explored:

Fine-tuning on domain-specific data: Training LMs on domain-specific datasets related to procedural texts and action modeling can help improve their understanding of relevant concepts and language patterns specific to planning domains.

Multi-task learning: Incorporating additional auxiliary tasks related to action prediction or plan generation during LM training can provide supplementary context that aids model performance on text-based planning tasks.

Hybrid models: Combining symbolic reasoning techniques with neural networks can leverage both structured knowledge representation (like PDDL) and deep learning capabilities to enhance overall system performance.

Interactive learning frameworks: Developing interactive frameworks where LMs receive feedback from users during action prediction iterations can refine model outputs based on human input.

Transfer learning strategies: Leveraging pre-trained models fine-tuned on general language understanding tasks followed by task-specific fine-tuning using smaller annotated datasets specific to procedural texts can boost LM performance.

How might the findings of PROC2PDDL contribute to advancements in AI systems beyond planning?

The findings from PROC2PDDL offer valuable insights into various aspects that are crucial for advancing AI systems beyond just traditional planning:

Improved natural language understanding: By analyzing how LMs perform at generating formal representations from textual descriptions like those found in PROC2PDDL, researchers gain a deeper understanding of challenges related to semantic parsing and natural language grounding.

Enhanced integration between symbolic reasoning & deep learning: The analysis sheds light on deficiencies faced by LMs when tasked with generating domain-specific programs like those represented by PDDLS; this knowledge paves the way for developing hybrid models that combine symbolic reasoning capabilities with neural network strengths.

Informing future dataset creation & evaluation methodologies: The dataset itself serves as a benchmark for evaluating state-of-the-art models' abilities regarding defining preconditions/effects; this sets a precedent for creating similar open-domain datasets that bridge gaps between natural language processing (NLP) research areas.

4Facilitating interdisciplinary research collaborations:: The intersection between procedural texts annotation experts trained specifically for this task opens avenues for collaboration across different disciplines such as NLP experts working alongside domain specialists familiar with formalized representations used within AI Planning contexts.