Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
First-Order Progression beyond Local-Effect and Normal Actions
Authors: Daxin Liu, Jens Claßen
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider a larger class of theories, called the acyclic ones, that strictly subsumes normal actions. In such theories, dependencies between non-local effect fluent predicates are allowed, as long as they do not contain any cycles. We prove progression to be equally first-order definable for this class. Furthermore, under similar but stronger assumptions than those made by Liu and Lakemeyer, we show that progression is efficient as well. |
| Researcher Affiliation | Academia | 1The University of Edinburgh 2Roskilde University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of release) to open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available datasets for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would involve specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or system-level training settings. |