Finite Based Contraction and Expansion via Models
Abstract
We propose a new paradigm for Belief Change in which the new information is represented as sets of models, while the agent's body of knowledge is represented as a finite set of formulae, that is, a finite base. The focus on finiteness is crucial when we consider limited agents and reasoning algorithms. Moreover, having the input as arbitrary set of models is more general than the usual treatment of formulas as input. In this setting, we define new Belief Change operations akin to traditional expansion and contraction, and we identify the rationality postulates that emerge due to the finite representability requirement. We also analyse different logics concerning compatibility with our framework.
Cite
Text
Guimarães et al. "Finite Based Contraction and Expansion via Models." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I5.25786Markdown
[Guimarães et al. "Finite Based Contraction and Expansion via Models." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/guimaraes2023aaai-finite/) doi:10.1609/AAAI.V37I5.25786BibTeX
@inproceedings{guimaraes2023aaai-finite,
title = {{Finite Based Contraction and Expansion via Models}},
author = {Guimarães, Ricardo and Ozaki, Ana and Ribeiro, Jandson S.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {6389-6397},
doi = {10.1609/AAAI.V37I5.25786},
url = {https://mlanthology.org/aaai/2023/guimaraes2023aaai-finite/}
}