bibtype	C - Conference Paper (international conference)
ARLID	0576517
utime	20240402214531.2
mtime	20231017235959.9
title (primary) (eng)	On Open Problems Associated with Conditioning in the Dempster-Shafer Belief Function Theory
specification	page_count 10 s. media_type E
serial	ARLID cav_un_epca*0576516 title Proceedings of the 23rd Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty page_num 1-10 publisher place Osaka, Japan name Osaka Metropolitan University year 2023 editor name1 Yoshifumi name2 Kusunoki editor name1 Václav name2 Kratochvíl editor name1 Masahiro name2 Inuiguchi editor name1 Ondřej name2 Čepek
keyword	belief functions
keyword	conditioning
keyword	composition
keyword	conditional independence
author (primary)	ARLID cav_un_auth*0101118 name1 Jiroušek name2 Radim institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept (eng) Department of Decision Making Theory department (cz) MTR department (eng) MTR full_dept Department of Decision Making Theory fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0216188 name1 Kratochvíl name2 Václav institution UTIA-B full_dept (cz) Matematická teorie rozhodování full_dept Department of Decision Making Theory department (cz) MTR department MTR full_dept Department of Decision Making Theory country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0275452 name1 Shenoy name2 P. P. country US
source	source_type PDF dokument url http://library.utia.cas.cz/separaty/2023/MTR/kratochvil-0576517.pdf
cas_special	project project_id GA21-07494S agency GA ČR country CZ ARLID cav_un_auth*0430801 abstract (eng) As in probability theory, graphical and compositional models in the Dempster-Shafer (D-S) belief function theory handle multidimensional belief functions applied to support inference for practical problems. Both types of models represent multidimensional belief functions using their low-dimensional marginals. In the case of graphical models, these marginals are usually conditionals, for compositional models, they are unconditional. Nevertheless, one must introduce some conditioning to compose unconditional belief functions and avoid double-counting knowledge. Thus, conditioning is crucial in processing multidimensional compositional models for belief functions.\n\nThis paper summarizes some important open problems, the solution of which should enable a trouble-free design of computational processes employing D-S belief functions in AI. For some of them, we discuss possible solutions. The problems considered in this paper are of two types. There are still some gaps that should be filled to get a mathematically consistent uncertainty theory. Other problems concern the computational tractability of procedures arising from the super-exponential growth of the space and time complexity of the designed algorithms. action ARLID cav_un_auth*0456493 name Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty 20232 /23./ dates 20230916 mrcbC20-s 20230920 place Matsuyama country JP RIV BA FORD0 10000 FORD1 10100 FORD2 10103 reportyear 2024 num_of_auth 3 presentation_type PR inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0346458 confidential S arlyear 2023 mrcbU14 SCOPUS mrcbU24 PUBMED mrcbU34 WOS mrcbU56 PDF dokument mrcbU63 cav_un_epca*0576516 Proceedings of the 23rd Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty Osaka Metropolitan University 2023 Osaka, Japan 1 10 mrcbU67 Yoshifumi Kusunoki 340 mrcbU67 Václav Kratochvíl 340 mrcbU67 Masahiro Inuiguchi 340 mrcbU67 Ondřej Čepek 340