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Representing states in iterated belief revision (2305.09200v3)

Published 16 May 2023 in cs.AI

Abstract: Iterated belief revision requires information about the current beliefs. This information is represented by mathematical structures called doxastic states. Most literature concentrates on how to revise a doxastic state and neglects that it may exponentially grow. This problem is studied for the most common ways of storing a doxastic state. All four methods are able to store every doxastic state, but some do it in less space than others. In particular, the explicit representation (an enumeration of the current beliefs) is the more wasteful on space. The level representation (a sequence of propositional formulae) and the natural representation (a history of natural revisions) are more compact than it. The lexicographic representation (a history of lexicographic revision) is even more compact than them.

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Authors (1)

  1. Paolo Liberatore