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..
Tight Bounds for HTN Planning with Task Insertion
Authors: Ron Alford, Pascal Bercher, David W. Aha
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We lower that bound proving NEXPTIME-completeness and further prove tight complexity bounds along two axes: whether variables are allowed in method and action schemas, and whether methods must be totally ordered. We also introduce a new planning technique called acyclic progression, which we use to define provably efficient TIHTN planning algorithms. |
| Researcher Affiliation | Academia | Ron Alford ASEE/NRL Postdoctoral Fellow Washington, DC, USA EMAIL Pascal Bercher Ulm University Ulm, Germany EMAIL David W. Aha U.S. Naval Research Laboratory Washington, DC, USA EMAIL |
| Pseudocode | No | The paper describes the acyclic progression procedure in text but does not provide it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | No | This is a theoretical paper and does not use or train on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |