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..
Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively
Authors: Serena Booth, Christian Muise, Julie Shah
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a user study to evaluate whether knowledge compilation can aid logic interpretability. We find only sparse effects of knowledge compilation properties on interpretability. We discover some languages considered to be compilation-only are acceptable, while disjunctive normal form a representation assumed to be interpretable is not significantly more interpretable than other forms. |
| Researcher Affiliation | Collaboration | Serena Booth1 , Christian Muise2,3 and Julie Shah1 1MIT Computer Science and Arti๏ฌcial Intelligence Laboratory 2IBM Research 3MIT-IBM Watson AI Lab EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our study procedures and source code are available at github.com/serenabooth/logic-interpretability. |
| Open Datasets | No | The paper describes generating data through simulations ('We simulate the agents and generate traces...') but does not provide concrete access information (link, DOI, repository, or formal citation to a public dataset) for this data or specify the use of a well-known public dataset. |
| Dataset Splits | No | The paper describes a user study and scenario questions but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions participants completed the study 'on-site' but does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments or simulations. |
| Software Dependencies | No | The paper mentions software tools like 'PMC PREPROCESSOR' and 'DSHARP' but does not provide specific version numbers for these or any other ancillary software components needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the setup of a user study (e.g., number of questions, presentation format) but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for any computational models. |