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..
Social Hierarchical Learning
Authors: Bradley Hayes
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In work under review, I present a proof-of-concept goal inference solution, using hierarchical Hidden Markov Models directly constructed from the generated task hierarchy, achieving multi-resolution goal inference improving state estimation accuracy while maintaining the computational bene๏ฌts expected of hierarchical approaches. |
| Researcher Affiliation | Academia | Bradley Hayes Department of Computer Science Yale University EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper does not provide 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 (percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or detailed computer specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were provided in the paper. |
| Experiment Setup | No | No specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) were provided in the main text. |