Belief Horn merging operators: Characterization results and implementations

The paper contributes to the study of belief merging in many-valued logics, taking both a theoretical and implementation approach motivated by applications in real-world scenarios. We focus, in particular, on the Horn fragment of signed logic. On the one hand, a characterization of the class of models of a signed regular Horn formula and a sufficient condition for a signed Horn merging operator to satisfy logical IC-postulates are provided. On the other hand, an implementation of the belief merging process in the signed regular Horn fragment is presented.

Authors

Institutions

Publication Details

Journal
Fuzzy Sets and Systems
Published
2024-08-08
DOI
https://doi.org/10.1016/j.fss.2024.109099
Citations
1
Primary Topic
Logic, Reasoning, and Knowledge
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Belief Horn merging operators: Characterization results and implementations

Pilar Dellunde, Daniel Rivas-Barragan, Vicent Costa
1 citations
Fuzzy Sets and Systems
Logic, Reasoning, and Knowledge
0.35
article

Belief Horn merging operators: Characterization results and implementations

Pilar Dellunde, Daniel Rivas-Barragan, Vicent Costa
article en
1 citations

Abstract

The paper contributes to the study of belief merging in many-valued logics, taking both a theoretical and implementation approach motivated by applications in real-world scenarios. We focus, in particular, on the Horn fragment of signed logic. On the one hand, a characterization of the class of models of a signed regular Horn formula and a sufficient condition for a signed Horn merging operator to satisfy logical IC-postulates are provided. On the other hand, an implementation of the belief merging process in the signed regular Horn fragment is presented.

Fuzzy Sets and SystemsVol. 497
Universitat Autònoma de Barcelona (ES), Barcelona Graduate School of Mathematics (ES), Artificial Intelligence Research Institute (ES)
Openalex Percentile: Top 35%
Logic, Reasoning, and Knowledge
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.