A Counterfactual Approach to Energy Poverty Mitigation: A Case Study for Australia (Preliminary Report)
Abstract
Inferring user profiles is a research area of interest with applications in, e.g., recommendation systems, and cognitive rehabilitation. User profiles are being produced mostly from data mining and machine learning approaches, which brings the challenge of providing explanations about the creation, representation, and dynamics of user profiles. This project aims to suggest a novel knowledge-based approach for user profiling and its dynamics. Thus, we suggest the use of AGM-based belief revision to dynamic of profiles to (a) provide a formal representation of user profiles; (b) describe the changes in the profiles; (c) identify what caused the changes, and (d) return the sequence of changes that will transform an original profile into a target profile. The suggested approach will be tested in real-world applications. In this view, this project will provide a significant contribution to the Explainable Artificial Intelligence area, with applications in, e.g., cognitive rehabilitation, and human-computer interaction. (Note: Abstract adapted from conference context).
More information
- Authors
- Diogo Nuno Freitas; Eduardo Fermé; Santiago Budría
- Date
- 2025
- Conference
- Workshop on the Foundations and Future of Change in Artificial Intelligence (FCAI 2025) at the 28th European Conference on Artificial Intelligence (ECAI 2025)
- Location
- Bologna, Italy
- Source
- Link
Citation
@inproceedings{freitas2025counterfactual,
author = {Freitas, Diogo Nuno and Fermé, Eduardo and Budría, Santiago},
booktitle = {Workshop on the Foundations and Future of Change in Artificial Intelligence (FCAI 2025) at the 28th European Conference on Artificial Intelligence (ECAI 2025)},
month = {10},
title = {A Counterfactual Approach to Energy Poverty Mitigation: A Case Study for Australia (Preliminary Report)},
year = {2025},
url = {https://ceur-ws.org/Vol-4069/paper3.pdf}
}
