Finding explanations of inconsistency in multi-context systems

Abstract

We provide two approaches for explaining inconsistency in multi-context systems, where decentralized and heterogeneous system parts interact via nonmonotonic bridge rules. Inconsistencies arise easily in such scenarios, and nonmonotonicity calls for specific methods of inconsistency analysis. Both our approaches characterize inconsistency in terms of involved bridge rules: either by pointing out rules which need to be altered for restoring consistency, or by finding combinations of rules which cause inconsistency. We show duality and modularity properties, give precise complexity characterizations, and provide algorithms for computation using HEXprograms. Our results form a basis for inconsistency management in heterogeneous knowledge integration systems.

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 each H ⊆ IN i it is required that kb i ∪ H ∈ KB Li . Similar to IN i , OUT i denotes output beliefs of context C i , which are those beliefs p in BS i that occur in some bridge rule body in br M as "(i:p)" or as "not (i:p)" (see also [13]). By br M = n i=1 br i we denote the set of all bridge rules of M , and by ctx M = {C 1 , .…”

“…In particular, we specify input for policy reasoning, in terms of reserved predicates. These predicates encode inconsistency analysis results in terms of [13]. Furthermore, we specify action predicates that can be derived in rules.…”

“…Such inconsistencies are undesired, as (under common principles) inference becomes trivial. Explaining reasons for inconsistency in MCSs has been investigated in [13]: several independent inconsistencies can exist in a MCS, and each inconsistency usually comes with more than one possibility to repair it.…”

Modeling and Reasoning about Preference-Based Context-Aware Agents over Heterogeneous Knowledge Sources

“…In this connection, the notion of multi-context systems has been used for interlinking different knowledge sources in order to enhance the expressive capabilities of heterogeneous systems. A multi-context system (MCS) includes a set of contexts and a set of inference rules that allows information to flow among different contexts [7]. In MCS, each context is defined as a self-contained knowledge source which includes the set of axioms and inference rules to model the system and perform local reasoning.…”