Markov Argumentation Random Fields
Authors: Yuqing Tang, Nir Oren, Katia Sycara
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate an implementation of Markov Argumentation Random Fields (MARFs)... We have evaluated our implementation in the domain of intelligence analysis... |
| Researcher Affiliation | Academia | 1 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA 2 Department of Computing Science, University of Aberdeen, Aberdeen, UK |
| Pseudocode | No | The paper refers to algorithms used ('message-passing and MC-SAT') but does not provide pseudocode or explicit algorithm blocks for its own methodology. |
| Open Source Code | No | The paper mentions 'MARF software' and its components but does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper refers to the 'ELICIT task (Chan and Adali 2012)' and describes the nature of its 'facts', but it does not provide concrete access information (link, DOI, or a citation explicitly for a public dataset) for the data used in the evaluation. |
| Dataset Splits | No | The paper describes the nature of the information used in the ELICIT task but does not provide specific details on dataset splits (e.g., percentages, sample counts, or methodology for creating splits) for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments or the MARF software. |
| Software Dependencies | No | The paper mentions adapting 'message-passing and MC-SAT' algorithms but does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, or frameworks with versions) used for the implementation. |
| Experiment Setup | No | The paper mentions specific arbitrary weights used for demonstration (e.g., '1.5, 1.0, 1.0, 0.0' and updated to '3.0, 1.0, 1.0, 0') but does not provide a comprehensive set of experimental setup details such as hyperparameter values, optimization settings, or training configurations. |