Generality in artificial intelligence
Communications of the ACM
A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
A Reasoning Model Based on the Production of Acceptable Arguments
Annals of Mathematics and Artificial Intelligence
Modelling and Using Imperfect Context Information
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Comparing formal theories of context in AI
Artificial Intelligence
Argumentation Semantics for Defeasible Logic
Journal of Logic and Computation
Preference-based argumentation: Arguments supporting multiple values
International Journal of Approximate Reasoning
Reasoning with Inconsistencies in Propositional Peer-to-Peer Inference Systems
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Minimal and absent information in contexts
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inconsistency tolerance in P2P data integration: an epistemic logic approach
DBPL'05 Proceedings of the 10th international conference on Database Programming Languages
Reasoning with imperfect context and preference information in multi-context systems
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Rule-based contextual reasoning in ambient intelligence
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Partial preferences and ambiguity resolution in contextual defeasible logic
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
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The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. Most current Ambient Intelligence systems have not successfully addressed these challenges, as they rely on simplifying assumptions, such as perfect knowledge of context, centralized context, and unbounded computational and communicating capabilities. This paper presents a knowledge representation model based on the Multi-Context Systems paradigm, which represents ambient agents as autonomous logic-based entities that exchange context information through mappings, and uses preference information to express their confidence in the imported knowledge. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve conflicts caused by the interaction of ambient agents through mappings, and a distributed algorithm for query evaluation.