Intention is choice with commitment
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Autonomous Agents and Multi-Agent Systems
On the revision of preferences and rational inference processes
Artificial Intelligence
Database querying under changing preferences
Annals of Mathematics and Artificial Intelligence
Dynamic Epistemic Logic
From Belief Change to Preference Change
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
Hidden uncertainty in the logical representation of desires
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Preferences in AI: An overview
Artificial Intelligence
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Various tasks need to consider preferences in a dynamic way. To evaluate and classify methods for preference change, we introduce eight properties for preferences evolving after some new fact has been learned. Four properties are concerned with persistence of preferences when something being preferred is (partly) satisfied or dissatisfied, and formalize that preference change indicates that the ideal state has not been reached or has become unreachable. Four other properties are concerned with persistence of preferences when, roughly, the agent learns something she already expected to hold, and formalizes that preference change is due to surprise. We define a family of preference change operators, parameterized by a revision function on epistemic states and a semantics for interpreting preferences over formulas, and we give conditions on the revision function and the semantics of preference for each of the eight conditions to hold.