Social Hierarchical Learning
Authors: Bradley Hayes
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 beneļ¬ts expected of hierarchical approaches. |
| Researcher Affiliation | Academia | Bradley Hayes Department of Computer Science Yale University bradley.h.hayes@yale.edu |
| 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. |