Approximations for explanations of inconsistency in partially known multi-context systems

Authors:
Thomas Eiter;Michael Fink;Peter Schüller
Affiliations:
Institute of Information Systems, Vienna University of Technology, Vienna, Austria;Institute of Information Systems, Vienna University of Technology, Vienna, Austria;Institute of Information Systems, Vienna University of Technology, Vienna, Austria
Venue:
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Year:
2011

Citing 14
Cited 0

A theory of the learnable

Communications of the ACM
Cause-effect relationships and partially defined Boolean functions

Annals of Operations Research
Multilanguage hierarchical logics, or: how we can do without modal logics

Artificial Intelligence
Tractable reasoning via approximation

Artificial Intelligence
Knowledge compilation and theory approximation

Journal of the ACM (JACM)
Queries and Concept Learning

Machine Learning
Queries and Concept Learning

Machine Learning
Distributed Defeasible Contextual Reasoning in Ambient Computing

AmI '08 Proceedings of the European Conference on Ambient Intelligence
Equilibria in heterogeneous nonmonotonic multi-context systems

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Notes on formalizing context

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Contextual default reasoning

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active probing strategies for problem diagnosis in distributed systems

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Focusing on probable diagnoses

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Rule-based policy representation and reasoning for the semantic web

RW'07 Proceedings of the Third international summer school conference on Reasoning Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-context systems are a formalism to interlink decentralized and heterogeneous knowledge based systems (contexts), which interact via possibly nonmonotonic bridge rules. Inconsistency is a major problem, as it renders such systems useless. In applications involving confidentiality or trust, it is likely that complete knowledge about all system parts is unavailable. To address inconsistency in such scenarios, we extend existing notions for characterizing inconsistency in multi-context systems: we propose a representation of partial knowledge, and introduce a formalismfor approximating reasons of inconsistency. We also discuss query selection strategies for improving approximations in situations where a limited number of queries can be posed to a partially known context.