Decomposition of distributed nonmonotonic multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
The DMCS solver for distributed nonmonotonic multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Comparing inconsistency resolutions in multi-context systems
ESSLLI'10 Proceedings of the 2010 international conference on New Directions in Logic, Language and Computation
Context-Aware Multi-Agent Planning in intelligent environments
Information Sciences: an International Journal
DEAL: A Distributed Authorization Language for Ambient Intelligence
International Journal of Ambient Computing and Intelligence
A rule-based contextual reasoning platform for ambient intelligence environments
RuleML'13 Proceedings of the 7th international conference on Theory, Practice, and Applications of Rules on the Web
Journal of Network and Computer Applications
| Hi-index | 0.00 |
Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges in the study of Distributed Artificial Intelligence. This paper proposes a solution based on the Multi-Context Systems paradigm, according to which local knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To resolve potential inconsistencies that may arise from the interaction of contexts through their mappings (global conflicts), we use a preference ordering on the system contexts, which may express the confidence that an agent has in the knowledge imported by other agents. On top of this model, we have developed four alternative strategies for global conflicts resolution, which mainly differ in the type and extent of context and preference information that is used to resolve potential conflicts. The four strategies have been respectively implemented in four versions of a distributed algorithm for query evaluation and evaluated in a simulated P2P system.