Imaginary Kinematics

Sabina Marchetti, Alessandro Antonucci
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:103-112, 2018.

Abstract

We introduce a novel class of adjustment rules for a collection of beliefs. This is an exten- sion of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike stan- dard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the func- tionals we introduce are based on the imagi- nary counterpart of probability kinematics for standard belief revision, and prove that, under certain conditions, all standard postulates for belief revision are satisfied.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-marchetti18a, title = {Imaginary Kinematics}, author = {Marchetti, Sabina and Antonucci, Alessandro}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {103--112}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/marchetti18a/marchetti18a.pdf}, url = {https://proceedings.mlr.press/r16/marchetti18a.html}, abstract = {We introduce a novel class of adjustment rules for a collection of beliefs. This is an exten- sion of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike stan- dard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the func- tionals we introduce are based on the imagi- nary counterpart of probability kinematics for standard belief revision, and prove that, under certain conditions, all standard postulates for belief revision are satisfied.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Imaginary Kinematics %A Sabina Marchetti %A Alessandro Antonucci %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-marchetti18a %I PMLR %P 103--112 %U https://proceedings.mlr.press/r16/marchetti18a.html %V R16 %X We introduce a novel class of adjustment rules for a collection of beliefs. This is an exten- sion of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike stan- dard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the func- tionals we introduce are based on the imagi- nary counterpart of probability kinematics for standard belief revision, and prove that, under certain conditions, all standard postulates for belief revision are satisfied. %Z Reissued by PMLR on 04 October 2026.
APA
Marchetti, S. & Antonucci, A.. (2018). Imaginary Kinematics. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:103-112 Available from https://proceedings.mlr.press/r16/marchetti18a.html. Reissued by PMLR on 04 October 2026.

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