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..
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Authors: Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni5592-5599
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate EAI and EDS in a simulation setting and in a realistic case study concerning healthy eating habits. Results are promising in both cases, but EDS is more effective at predicting useful utility functions. |
| Researcher Affiliation | Academia | 1 Free University of Bozen-Bolzano, Italy 2 University College London, United Kingdom 3 Fondazione Bruno Kessler, Italy 4 University of Trento, Italy |
| Pseudocode | Yes | Algorithm 1: Sim Dialogue(T, L, up, uo, δ) |
| Open Source Code | Yes | The source code and the supplementary material are online at shorturl.at/oy KV3 |
| Open Datasets | No | The paper describes the generation of synthetic datasets and the creation of a dataset from user profiles, but it does not provide concrete access information (e.g., URL, DOI, or formal citation to an existing public dataset) for a publicly available or open dataset. |
| Dataset Splits | Yes | We use the k-fold cross validation technique. The dataset Uo T,i is split into k parts, k 1 parts are used as training set for Sim Dialogue(ML) and the remaining part is left as test set for both Sim Dialogue(ML) and Sim Dialogue. In this way, k splits/folds of the original dataset Uo T,i are obtained and for each split we run both Sim Dialogue(ML) and Sim Dialogue. ... k = 5 for the k-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like SVR, KMeans, and Random Forest, but does not provide specific version numbers for these or other dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | The hyperparameters for SVR are C = 1, ϵ = 0.1 and the radial basis function as a kernel. ... The random forest in CRAMER has 100 estimators with the minimum number of samples required: i) to split a node is 2, ii) to be a leaf is 1. ... Other parameters have a single value: the number of synthetic trees (|T| = 10) and datasets (|Uo T | = 10), the size of Uo T,i is 2000, the cluster variance σ2 C is 1.0, the discount factor δ in Bimaximax is 1 as it is not relevant for the simulations and k = 5 for the k-fold cross validation. |