Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Argumentation Semantics for Defeasible Logic
Journal of Logic and Computation
A Distributed Argumentation Framework using Defeasible Logic Programming
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Contextual Argumentation in Ambient Intelligence
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Argumentation Context Systems: A Framework for Abstract Group Argumentation
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
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
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
Defeasible Contextual Reasoning with Arguments in Ambient Intelligence
IEEE Transactions on Knowledge and Data Engineering
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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
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Domains, such as Ambient Intelligence and Social Networks, are characterized by some common features including distribution of the available knowledge, entities with different backgrounds, viewpoints and operational environments, and imperfect knowledge. Multi-Context Systems (MCS) has been proposed as a natural representation model for such environments, while recent studies have proposed adding non-monotonic features to MCS to address the issues of incomplete, uncertain and ambiguous information. In previous works, we introduced a non-monotonic extension to MCS and an argument-based reasoning model that handle imperfect context information based on defeasible argumentation. Here we propose alternative variants that integrate features such as partial preferences, ambiguity propagating and team defeat, and study the relations between the different variants in terms of conclusions being drawn in each case.