A characterization theorem for trackable updates

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

The information available to some agents can be represented with several mathematical models, depending on one’s purpose. These models differ not only in their level of precision, but also in how they evolve when the agents receive new data. The notion of tracking was introduced to describe the matching of information dynamics, or ‘updates’, on different structures. We expand on the topic of tracking, focusing on the example of plausibility and evidence models, two central structures in the literature on formal epistemology. Our main result is a characterization of the trackable updates of a certain class, that is, we give the exact condition for an update on evidence models to be trackable by a an update on plausibility models. For the positive cases we offer a procedure to compute the other update, while for the negative cases we give a recipe to construct a counterexample to tracking. To our knowledge, this is the first result of this kind in the literature.

Original languageEnglish
Title of host publicationBusiness Process Management - 15th International Conference, BPM 2017, Proceedings
EditorsJeremy Seligman, Tomoyuki Yamada, Alexandru Baltag
PublisherSpringer - Verlag
Pages94-107
Number of pages14
ISBN (Print)9783319649993
DOIs
Publication statusPublished - 2017
Event15th International Conference on Business Process Management, BPM 2017 - Barcelona, Spain
Duration: 10 Sept 201715 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10445 LNCS

Conference

Conference15th International Conference on Business Process Management, BPM 2017
Country/TerritorySpain
CityBarcelona
Period10/09/201715/09/2017

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