Generality in artificial intelligence
Communications of the ACM
A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Contexts: a formalization and some applications
Contexts: a formalization and some applications
Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
Local models semantics, or contextual reasoning = locality + compatibility
Artificial Intelligence
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
A Reasoning Model Based on the Production of Acceptable Arguments
Annals of Mathematics and Artificial Intelligence
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
Distributed Defeasible Contextual Reasoning in Ambient Computing
AmI '08 Proceedings of the European Conference on Ambient Intelligence
Contextual Argumentation in Ambient Intelligence
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
Relational information exchange and aggregation in multi-context systems
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
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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Multi-Context Systems (MCS) are logical formalizations of distributed context theories connected through a set of mapping rules, which enable information flow between different contexts. Recent studies have proposed adding non-monotonic features to MCS to handle problems such as incomplete, uncertain or ambiguous context information. In previous work, we proposed a non-monotonic extension to MCS and an argument-based reasoning model that enable handling cases of imperfect context information based on defeasible reasoning. To deal with ambiguities that may arise from the interaction of context theories through mappings, we used a preference relation, which is represented as a total ordering on the system contexts. Here, we extend this approach to additionally deal with incomplete preference information. To enable this, we replace total preference ordering with partial ordering, and modify our argumentation framework and the distributed algorithms that we previously proposed to meet the new requirements.