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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |