Preference-Based Inconsistency Management in Multi-Context Systems (Extended Abstract)

Preference-Based Inconsistency Management in Multi-Context Systems (Extended Abstract)

Thomas Eiter, Antonius Weinzierl

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Journal track. Pages 5593-5597. https://doi.org/10.24963/ijcai.2018/791

Establishing information exchange between existing knowledge-based systems can lead to devastating inconsistency. Automatic resolution of inconsistency often is unsatisfactory, because any modification of the information flow may lead to bad or even dangerous conclusions. Methods to identify and select preferred repairs of inconsistency are thus needed. In this work, we leverage the expressive power and generality of Multi-Context Systems (MCS), a formalism for information exchange, to select most preferred repairs, by use of a meta-reasoning transformation. As for computational complexity, finding preferred repairs is not higher than the base case; finding most-preferred repairs is higher, yet worst-case optimal.
Keywords:
Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning
Knowledge Representation and Reasoning: Knowledge Representation Languages
Knowledge Representation and Reasoning: Non-monotonic Reasoning