Abstract
We study a model of preference revision in which a prior preference over a set of alternatives is adjusted in order to accommodate input from an authoritative source, while maintaining certain structural constraints (e.g., transitivity, completeness), and without giving up more information than strictly necessary. We analyze this model under two aspects: the first allows us to capture natural distance-based operators, at the cost of a mismatch between the input and output formats of the revision operator. Requiring the input and output to be aligned yields a second type of operator, which we characterize using preferences on the comparisons in the prior preference Prefence revision is set in a logic-based framework and using the formal machinery of belief change, along the lines of the well-known AGM approach: we propose rationality postulates for each of the two versions of our model and derive representation results, thus situating preference revision within the larger family of belief change operators.
Original language | English |
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Title of host publication | Proceedings AAAI 2022 |
Publisher | AAAI Press |
Pages | 5676-5683 |
DOIs | |
Publication status | Published - 2022 |
Event | 36th AAAI Conference on Artificial Intelligence: AAAI 2022 - Vancouver, Canada Duration: 22 Feb 2022 → 1 Mar 2022 |
Conference
Conference | 36th AAAI Conference on Artificial Intelligence |
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Abbreviated title | AAAI 2022 |
Country/Territory | Canada |
City | Vancouver |
Period | 22/02/22 → 1/03/22 |