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 extension of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike standard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the functionals we introduce are based on the imaginary 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 extension of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike standard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the functionals we introduce are based on the imaginary 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 extension of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike standard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the functionals we introduce are based on the imaginary 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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