A logic-based theory of deductive arguments
Artificial Intelligence
A Textbook of Belief Dynamics: Solutions to Exercises
A Textbook of Belief Dynamics: Solutions to Exercises
Explanations, belief revision and defeasible reasoning
Artificial Intelligence
Defeasible logic programming: an argumentative approach
Theory and Practice of Logic Programming
A negotiation-style framework for non-prioritised revision
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Argumentation in artificial intelligence
Artificial Intelligence
Negotiation as mutual belief revision
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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The success postulate of classic belief revision theory demands that after revising some beliefs with by information the new information is believed. However, this form of prioritized belief revision is not apt under many circumstances. Research in non-prioritized belief revision investigates forms of belief revision where success is not a desirable property. Herein, selective revision uses a two step approach, first applying a transformation function to decide if and which part of the new information shall be accepted, and second, incorporating the result using a prioritized revision operator. In this paper, we implement a transformation function by employing deductive argumentation to assess the value of new information. Hereby we obtain a non-prioritized revision operator that only accepts new information if believing in the information is justifiable with respect to the beliefs. By making use of previous results on selective revision we prove that our revision operator satisfies several desirable properties. We illustrate the use of the revision operator by means of examples and compare it with related work.