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Belief Expansion in Subset Models

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Logical Foundations of Computer Science (LFCS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11972))

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Abstract

Subset models provide a new semantics for justifcation logic. The main idea of subset models is that evidence terms are interpreted as sets of possible worlds. A term then justifies a formula if that formula is true in each world of the interpretation of the term.

In this paper, we introduce a belief expansion operator for subset models. We study the main properties of the resulting logic as well as the differences to a previous (symbolic) approach to belief expansion in justification logic.

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Acknowledgements

This work was supported by the Swiss National Science Foundation grant 200020_184625.

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Authors and Affiliations

  1. Institute of Computer Science, University of Bern, Bern, Switzerland

    Eveline Lehmann & Thomas Studer

Authors
  1. Eveline Lehmann
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  2. Thomas Studer
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Corresponding author

Correspondence to Eveline Lehmann .

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Editors and Affiliations

  1. The Graduate Center, CUNY, New York, NY, USA

    Sergei Artemov

  2. Cornell University, Ithaca, NY, USA

    Anil Nerode

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Lehmann, E., Studer, T. (2020). Belief Expansion in Subset Models. In: Artemov, S., Nerode, A. (eds) Logical Foundations of Computer Science. LFCS 2020. Lecture Notes in Computer Science(), vol 11972. Springer, Cham. https://doi.org/10.1007/978-3-030-36755-8_6

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