Preference-Based Inconsistency Assessment in Multi-Context Systems

Abstract

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.

Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

“…Building on previous work on preferencebased inconsistency resolution in MCS [2,12,13], we will develop algorithms for preference-based coalition formation in the presence of conflicting goals. We also plan to extend our approach with elements of dynamic MCS [10], i.e.…”

The IMPL Policy Language for Managing Inconsistency in Multi-Context Systems

“…For instance, to encode preference relations (e.g., as in [14]) between system parts, diagnoses, or explanations, an atom preferredContext(c 1 , c 2 ) could denote that context c 1 is considered more reliable than context c 2 . The extensions of such auxiliary predicates need to be defined by the rules of the policy (ordinary predicates) or provided by the implementation (built-in predicates), i.e., the 'solver' used to evaluate the policy.…”

Semantic Reasoning with SPARQL in Heterogeneous Multi-context Systems

“…These issues are not addressed in a satisfactory way by existing proposals to add variables to MCSs: in [7], variables are seen as schematic variables only, i.e., a bridge rule is only a short notation for the set of instantiated (thus variable-free) bridge rules. In [8], bridge rules with variables require that the contexts of the MCS adhere to a given syntax for predicates; and similar in [13] where only first order languages are considered.…”