Temporal reasoning based on semi-intervals
Artificial Intelligence
Integration of weighted knowledge bases
Artificial Intelligence
The size of a revised knowledge base
Artificial Intelligence
Combining topological and size information for spatial reasoning
Artificial Intelligence
Artificial Intelligence - Special issue on nonmonotonic reasoning
Merging taxonomies under RCC-5 algebraic articulations
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Merging Qualitative Constraints Networks Using Propositional Logic
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
A new tractable subclass of the rectangle algebra
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Merging Qualitative Constraint Networks in a Piecewise Fashion
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Reasoning about qualitative temporal information
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Aggregation Operators and Commuting
IEEE Transactions on Fuzzy Systems
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We address the problem of merging qualitative constraint networks (QCNs) representing agents local preferences or beliefs on the relative position of spatial or temporal entities. Two classes of merging operators which, given a set of input QCNs defined on the same qualitative formalism, return a set of qualitative configurations representing a global view of these QCNs, are pointed out. These operators are based on local distances and aggregation functions. In contrast to QCN merging operators recently proposed in the literature, they take account for each constraint from the input QCNs within the merging process. Doing so, inconsistent QCNs do not need to be discarded at start, hence agents reporting locally consistent, yet globally inconsistent pieces of information (due to limited rationality) can be taken into consideration.