Stratified Evidence Logics

Philippe Balbiani, David Fernández-Duque, Andreas Herzig, Emiliano Lorini

IJCAI 2019 pp. 1523-1529

doi:10.24963/IJCAI.2019/211 /ijcai/2019/balbiani2019ijcai-stratified/

Abstract

Evidence logics model agents' belief revision process as they incorporate and aggregate information obtained from multiple sources. This information is captured using neighbourhood structures, where individual neighbourhoods represent pieces of evidence. In this paper we propose an extended framework which allows one to explicitly quantify either the number of evidence sets, or effort, needed to justify a given proposition, provide a complete deductive calculus and a proof of decidability, and show how existing frameworks can be embedded into ours.

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Text

Balbiani et al. "Stratified Evidence Logics." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/211

Markdown

[Balbiani et al. "Stratified Evidence Logics." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/balbiani2019ijcai-stratified/) doi:10.24963/IJCAI.2019/211

BibTeX

@inproceedings{balbiani2019ijcai-stratified,
  title     = {{Stratified Evidence Logics}},
  author    = {Balbiani, Philippe and Fernández-Duque, David and Herzig, Andreas and Lorini, Emiliano},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {1523-1529},
  doi       = {10.24963/IJCAI.2019/211},
  url       = {https://mlanthology.org/ijcai/2019/balbiani2019ijcai-stratified/}
}