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..
Tractable Cost-Optimal Planning over Restricted Polytree Causal Graphs
Authors: Meysam Aghighi, Peter Jonsson, Simon Ståhlberg
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove tractability of cost-optimal planning by providing an algorithm based on a novel notion of variable isomorphism. Our algorithm also sheds light on the k-consistency procedure for identifying unsolvable planning instances. |
| Researcher Affiliation | Academia | Meysam Aghighi, Peter Jonsson and Simon St ahlberg Department of Computer and Information Science Link oping University Link oping, Sweden {meysam.aghighi, peter.jonsson, simon.stahlberg} at liu.se |
| Pseudocode | Yes | Figure 1: The algorithm devised in this paper, which outputs a new instance with a single variable of constant size. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information about public dataset availability is provided. |
| Dataset Splits | No | The paper describes a theoretical algorithm and does not involve empirical evaluation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes a theoretical algorithm and does not report on experimental hardware specifications. |
| Software Dependencies | No | The paper describes a theoretical algorithm and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical algorithm design and analysis, and therefore does not provide details on experimental setup, hyperparameters, or training configurations. |