Preference-based inconsistency assessment in multi-context systems

Authors:
Thomas Eiter;Michael Fink;Antonius Weinzierl
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:
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
Year:
2010

Citing 6
Cited 2

Multilanguage hierarchical logics, or: how we can do without modal logics

Artificial Intelligence
Argumentation Context Systems: A Framework for Abstract Group Argumentation

LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Equilibria in heterogeneous nonmonotonic multi-context systems

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements

Journal of Artificial Intelligence Research
Contextual default reasoning

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Preference-based configuration of web page content

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2

Nonmonotonic multi-context systems: a flexible approach for integrating heterogeneous knowledge sources

Logic programming, knowledge representation, and nonmonotonic reasoning
Comparing inconsistency resolutions in multi-context systems

ESSLLI'10 Proceedings of the 2010 international conference on New Directions in Logic, Language and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Resolving inconsistency in knowledge-integration systems is a major issue, especially when interlinking heterogeneous, autonomous sources. The latter can be done using a multi-context system, also in presence of non-monotonicity. Recent work considered diagnosis and explanation of inconsistency in such systems in terms of faulty information exchange. To discriminate between different solutions, we consider inconsistency assessment using preference. We present means to a) filter undesired diagnoses b) select the most preferred ones given an arbitrary preference order and c) use CP-nets for efficient selection. Furthermore, we show how to incorporate the assessment into a Multi-Context System by a transformational approach. In a range of settings, the complexity does not increase compared to the basic case and key properties like decentralized information exchange and information hiding are preserved.