An Alternative Proof Method for Possibilistic Logic and Its Application to Terminological Logics
Abstract
Possibilistic logic, an extension of first-order logic, deals with uncertainty that can be estimated in terms of possibility and necessity measures. Syntactically, this means that a first-order formula is equipped with a possibility degree or a necessity degree that expresses to what extent the formula is possibly or necessarily true. Possibilistic resolution yields a calculus for possibilistic logic which respects the semantics developed for possibilistic logic. A drawback, which possibilistic resolution inherits from classical resolution, is that it may not terminate if applied to formulas belonging to decidable fragments of first-order logic. Therefore we propose an alternative proof method for possibilistic logic. The main feature of this method is that it completely abstracts from a concrete calculus but uses as basic operation a test for classical entailment. We then instantiate possibilistic logic with a terminological logic, which is a decidable subclass o f first-order logic but nevertheless much more expressive than propositional logic. This yields an extension of terminological logics towards the representation of uncertain knowledge which is satisfactory from a semantic as well as algorithmic point of view.
Cite
Text
Hollunder. "An Alternative Proof Method for Possibilistic Logic and Its Application to Terminological Logics." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/0888-613X(94)00015-UMarkdown
[Hollunder. "An Alternative Proof Method for Possibilistic Logic and Its Application to Terminological Logics." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/hollunder1994uai-alternative/) doi:10.1016/0888-613X(94)00015-UBibTeX
@inproceedings{hollunder1994uai-alternative,
title = {{An Alternative Proof Method for Possibilistic Logic and Its Application to Terminological Logics}},
author = {Hollunder, Bernhard},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {327-335},
doi = {10.1016/0888-613X(94)00015-U},
url = {https://mlanthology.org/uai/1994/hollunder1994uai-alternative/}
}