Ordered belief fusion in possibilistic logic

Authors:
Churn-Jung Liau
Affiliations:
Institute of Information Science, Academia Sinica, Taipei, Taiwan
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

Citing 8
Cited 0

Quantitative modal logic and possibilistic reasoning

ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Possibilistic logic

Handbook of logic in artificial intelligence and logic programming (vol. 3)
Reasoning about knowledge

Reasoning about knowledge
Possibilistic reasoning—a mini-survey and uniform semantics

Artificial Intelligence
Reasoning About Data Provided By Federated Deductive Databases

Journal of Intelligent Information Systems
Epistemic Logic for AI and Computer Science

Epistemic Logic for AI and Computer Science
A Logical Approach to Multi-Sources Reasoning

International Conference Logic at Work on Knowledge Representation and Reasoning Under Uncertainty, Logic at Work
A modal logic framework for multi-agent belief fusion

ACM Transactions on Computational Logic (TOCL)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a logical framework for reasoning about uncertain belief fusion. The framework is a combination of multi-agent epistemic logic and possibilistic logic. We use graded epistemic operators to represent agents' uncertain beliefs, and the operators are interpreted in accordance with possibilistic semantics. Ordered fusion can resolve the inconsistency caused by direct fusion. We consider two strategies to merge uncertain beliefs. In the first strategy, called level cutting fusion, if inconsistency occurs at some level, then all beliefs at the lower levels are discarded simultaneously. In the second, called level skipping fusion, only the level at which the inconsistency occurs is skipped. We present the formal semantics and axiomatic systems for these two strategies.