Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Graph Theory
Logic programming with social features1
Theory and Practice of Logic Programming
Equilibria in heterogeneous nonmonotonic multi-context systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Distributed reasoning in a peer-to-peer setting: application to the semantic web
Journal of Artificial Intelligence Research
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Backtracking through biconnected components of a constraint graph
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Aspects of distributed and modular ontology reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Strategies for contextual reasoning with conflicts in ambient intelligence
Knowledge and Information Systems
DRAGO: distributed reasoning architecture for the semantic web
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Symmetry breaking for distributed multi-context systems
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Relational information exchange and aggregation in multi-context systems
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
| Hi-index | 0.00 |
Multi-Context Systems (MCS) are formalisms that enable the inter-linkage of single knowledge bases, called contexts, via bridge rules. Recently, a fully distributed algorithm for evaluating heterogeneous, nonmonotonic MCS was described in [7]. In this paper, we continue this line of work and present a decomposition technique for MCS which analyzes the topology of an MCS. It applies pruning techniques to get economically small representations of context dependencies. Orthogonal to this, we characterize minimal interfaces for information exchange between contexts, such that data transmissions can be minimized. We then present a novel evaluation algorithm that operates on a query plan which is compiled with topology pruning and interface minimization. The effectiveness of the optimization techniques is demonstrated by a prototype implementation, which uses an off-the-shelf SAT solver and shows encouraging experimental results.