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..
Using Social Relationships to Control Narrative Generation
Authors: Julie Porteous, Fred Charles, Marc Cavazza
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Narrative Diversity Results of experiments reported in (Porteous, Charles, and Cavazza 2013), obtained across hundreds of runs of the system measured in terms of real-time performance and story diversity, demonstrate the potential for the approach to yield large differences between generated stories via moderate changes to the social network. |
| Researcher Affiliation | Academia | Julie Porteous and Fred Charles and Marc Cavazza School of Computing, Teesside University Middlesbrough, United Kingdom EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | The paper describes its own domain model ('the NETWORKING domain model has a cast of 10 doctors, 5 nurses, 3 patients and close to 100 narrative actions') but does not provide concrete access information (link, DOI, formal citation for public access) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper describes the system architecture and domain model, but it does not contain specific experimental setup details such as concrete hyperparameter values or training configurations. |