Unsupervised Learning of HTNs in Complex Adversarial Domains
Authors: Michael Leece
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |