Chapter

Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation

Volume 9060 of the series Lecture Notes in Computer Science pp 233-248

Preference-Based Diagnosis Selection in Multi-Context Systems

  • Thomas EiterAffiliated withKnowledge-based Systems Group, Institute of Information Systems, Vienna University of Technology
  • , Michael FinkAffiliated withKnowledge-based Systems Group, Institute of Information Systems, Vienna University of Technology
  • , Antonius WeinzierlAffiliated withKnowledge-based Systems Group, Institute of Information Systems, Vienna University of Technology

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Nonmonotonic Multi-Context Systems (MCS) provide a rigorous framework and flexible approach to represent and reason over interlinked, heterogeneous knowledge sources. Not least due to nonmonotonicity, however, an MCS may be inconsistent and resolving inconsistency is a major issue. Notions of diagnosis and inconsistency explanations have been developed for this purpose, considering the information exchange as the primary culprit. To discriminate between different possible solutions, we consider preference-based diagnosis selection. We develop a general meta-reasoning technique, i.e., an MCS transformation capable of full introspection on possible diagnoses, and we present a natural encoding of preferred diagnosis selection on top. Moreover, for the more involved notions of diagnosis utilized, we establish that the complexity does not increase. However, this does not carry over to selecting most preferred diagnoses as the encoding is not polynomial.