Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
A consistency-based approach for belief change
Artificial Intelligence
On Computing Belief Change Operations using Quantified Boolean Formulas
Journal of Logic and Computation
Negotiation as mutual belief revision
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Belief extrapolation (or how to reason about observations and unpredicted change)
Artificial Intelligence
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A general framework for minimisation-based belief change is presented. A problem instance is made up of an undirected graph, where a formula is associated with each vertex. For example, vertices may represent spatial locations, points in time, or some other notion of locality. Information is shared between vertices via a process of minimisation over the graph. We give equivalent semantic and syntactic characterisations of this minimisation. We also show that this approach is general enough to capture existing minimisation-based approaches to belief merging, belief revision, and (temporal) extrapolation operators. While we focus on a set-theoretic notion of minimisation, we also consider other approaches, such as cardinality-based and priority-based minimisation.