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]
Hierarchical Planning: Relating Task and Goal Decomposition with Task Sharing
Authors: Ron Alford, Vikas Shivashankar, Mark Roberts, Jeremy Frank, David W. Aha
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The aim of this work is to formally analyze the effects of these modifications to HTN semantics on the computational complexity and expressivity of HTN planning. To facilitate analysis, we unify goal and task planning into Goal-Task Network (GTN) planning. GTN models use HTN and HGN constructs, but have a solution-preserving mapping back to HTN planning. We then show theoretical results that provide new insights into both the expressivity as well as computational complexity of GTN planning under a number of different semantics. |
| Researcher Affiliation | Collaboration | Ron Alford,1 MITRE; Mc Lean, VA | EMAIL 2Knexus Research Corporation; National Harbor, MD | EMAIL 3NRC Postdoctoral Fellow; Naval Research Laboratory; Washington, DC | EMAIL 4NASA Ames Research Center; Moffett Field, CA | EMAIL 5Navy Center for Applied Research in AI; Naval Research Laboratory, Washington, DC | EMAIL |
| Pseudocode | No | The paper describes formal constructions and iterative processes (e.g., Construction 4.1, Construction 4.8) in prose and mathematical notation, but it does not present them as structured pseudocode blocks or explicitly labeled algorithms. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets. Therefore, it does not mention public dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, so it does not provide details on training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or computations that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments, therefore no experimental setup details like hyperparameters or training settings are provided. |