Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Authors: Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni5592-5599
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |