A Model-Based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic
Abstract
This paper presents a new approach for merging prior-itized knowledge bases in possibilistic logic. Our ap-proach is semantically defined by a model-based merg-ing operator in propositional logic and the merged result of our approach is a normal possibility distribution. We also give an algorithm to obtain the syntactical counter-part of the semantic approach. The logical properties of our approach are considered. Finally, we analyze the computational complexity of our merging approach.
Cite
Text
Qi. "A Model-Based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Qi. "A Model-Based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/qi2007aaai-model/)BibTeX
@inproceedings{qi2007aaai-model,
title = {{A Model-Based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic}},
author = {Qi, Guilin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {471-476},
url = {https://mlanthology.org/aaai/2007/qi2007aaai-model/}
}