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..
Unsupervised Learning of HTNs in Complex Adversarial Domains
Authors: Michael Leece
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thus far, I have worked on two approaches to the problem of learning HTNs from human demonstrations in the RTS domain. ... I have recently implemented an agent that uses HTN planning to play RTS games, but there are many confounding factors when using pure strength of gameplay as a metric. I would welcome advice on this topic from any mentor. |
| Researcher Affiliation | Academia | Michael Leece University of California, Santa Cruz mleece [at] ucsc.edu |
| Pseudocode | No | The paper describes algorithms in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code or make an explicit statement about its release. |
| Open Datasets | No | The paper mentions using 'a database of human replays' and 'a database of traces' but does not provide any concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |