Minimal change in evolving multi-context systems

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In open environments, agents need to reason with knowledge from various sources, possibly represented in different languages. The framework of Multi-Context Systems (MCSs) offers an expressive, yet flexible, solution since it allows for the integration of knowledge from different heterogeneous sources in an effective and modular way. However, MCSs are essentially static as they were not designed for dynamic scenarios. The recently introduced evolving Multi-Context Systems (eMCSs) extend MCSs by also allowing the system to both react to, and reason in the presence of dynamic observations, and evolve by incorporating new knowledge, thus making it even more adequate in Multi-Agent Systems characterised by their dynamic and open nature. In dynamic scenarios which admit several possible alternative evolutions, the notion of minimal change has always played a crucial role in determining the most plausible choice. However, different KR formalisms - as combined within eMCSs - may require different notions of minimal change, making their study and their interplay a relevant highly nontrivial problem. In this paper, we study the notion of minimal change in eMCSs, by presenting and discussing alternative minimal change criteria.

Original languageEnglish
Title of host publicationEPIA 2015
Subtitle of host publicationProgress in Artificial Intelligence
EditorsF. Pereira , P. Machado, E. Costa , A. Cardoso
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-319-23485-4
ISBN (Print)978-3-319-23484-7
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015


  • Artificial intelligence
  • Intelligent agents


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