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..
Decidability Results in First-Order Epistemic Planning
Authors: Andrés Occhipinti Liberman, Rasmus Kræmmer Rendsvig
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies the decidability of the plan existence problem for FODEL planning, showing that while the problem is generally undecidable, the cases of single-agent planning and multi-agent planning with non-modal preconditions are decidable. |
| Researcher Affiliation | Academia | Andr es Occhipinti Liberman1 and Rasmus Kræmmer Rendsvig2 1DTU Compute, Technical University of Denmark 2Center for Information and Bubble Studies, University of Copenhagen |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not use datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve data splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup. |