scholarly journals Jokes and Belief Revision

2020 ◽  
Author(s):  
Florence Dupin De Saint-Cyr ◽  
Henri Prade
Keyword(s):  
Artificial Intelligence ◽  
Belief Revision ◽  
Basic Mechanism ◽  
Formal Modeling ◽  
Revision Process ◽  
Possibilistic Logic ◽  
Preliminary Study ◽  
In The Beginning

The paper deals with a topic little studied in artificial intelligence: the understanding of humor. In this preliminary study, we try to identify the basic mechanism at work in quips and narrative jokes. It seems that in many cases a belief revision process is operating, leading to an unexpected conclusion, through the punchline of the jest. We propose a formal modeling of jokes based on belief revision. Namely the punchline, which triggers a revision, is both surprising and explains perfectly what was reported in the beginning of the joke. This also suggests a way of ranking jokes in terms of surprise and strength of explanation, using possibilistic logic.

scholarly journals Modeling Belief in Dynamic Systems, Part II: Revision and Update

1999 ◽  
Vol 10 ◽  
pp. 117-167 ◽  
Author(s):  
N. Friedman ◽  
J. Y. Halpern
Keyword(s):  
Artificial Intelligence ◽  
Belief Revision ◽  
Dynamic Systems ◽  
Belief Change ◽  
Companion Paper ◽  
Minimal Change ◽  
Special Cases ◽  
Epistemic Modalities ◽  
Over Time ◽  
New Framework

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.

scholarly journals Sa1235 APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PREDICTION OF INVASION DEPTH IN EARLY GASTRIC CANCER: PRELIMINARY STUDY

2018 ◽  
Vol 87 (6) ◽  
pp. AB176 ◽  
Author(s):  
Hong Jin Yoon ◽  
Seunghyup Kim ◽  
Jie-Hyun Kim ◽  
Ji-Soo Keum ◽  
Junik Jo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Invasion Depth ◽  
Preliminary Study

scholarly journals A Preliminary Study to Use SUVmax of FDG PET-CT as an Identifier of Lesion for Artificial Intelligence

2021 ◽  
Vol 8 ◽  
Author(s):  
Kenji Hirata ◽  
Osamu Manabe ◽  
Keiichi Magota ◽  
Sho Furuya ◽  
Tohru Shiga ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Local Maximum ◽  
Ct Images ◽  
Whole Body ◽  
Imaging Biomarkers ◽  
Pet Ct ◽  
Preliminary Study ◽  
Fdg Pet Ct

Background: Diagnostic reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily diagnostic reports of [18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) – computed tomography (CT). If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret diagnostic reports. We aimed to clarify whether the lesion can be localized using SUVmax strings.Methods: The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax = 3.14 was given, the voxels with 3.135 ≤ SUVmax < 3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results: A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax < 2), 79.5% (2 ≤ SUVmax < 5), and 97.8% (5 ≤ SUVmax) of lesions.Conclusion: In this preliminary study, SUVmax of FDG PET-CT could be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax < 2 were difficult to identify. The proposed method may have potential to make use of diagnostic reports retrospectively for constructing training datasets for AI.

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