Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Subjective Expert Evaluations in the Model-Theoretic Representation of Object Domain Knowledge. / Yakhyaeva, Gulnara; Skokova, Vera.
Artificial Intelligence - 19th Russian Conference, RCAI 2021, Proceedings. ed. / Sergei M. Kovalev; Sergei O. Kuznetsov; Aleksandr I. Panov. Springer Science and Business Media Deutschland GmbH, 2021. p. 152-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12948 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Subjective Expert Evaluations in the Model-Theoretic Representation of Object Domain Knowledge
AU - Yakhyaeva, Gulnara
AU - Skokova, Vera
N1 - Publisher Copyright: © 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Often, evaluative knowledge about the object area is formulated not only as an objective (statistical) probability but also as a subjective (expert) probability. Expert evaluations may be incomplete or inconsistent with each other. A tool is needed to check the consistency of expertise. The paper proposes a theoretical-modal formalization of subjective and objective interpretations of probability. This allows us to formulate the criteria for the correctness of the evaluative knowledge received from the experts. The article describes an algorithm for checking the correctness of evaluative knowledge, as well as an algorithm for correcting some incorrectness.
AB - Often, evaluative knowledge about the object area is formulated not only as an objective (statistical) probability but also as a subjective (expert) probability. Expert evaluations may be incomplete or inconsistent with each other. A tool is needed to check the consistency of expertise. The paper proposes a theoretical-modal formalization of subjective and objective interpretations of probability. This allows us to formulate the criteria for the correctness of the evaluative knowledge received from the experts. The article describes an algorithm for checking the correctness of evaluative knowledge, as well as an algorithm for correcting some incorrectness.
KW - Evaluative knowledge
KW - Fuzzification
KW - Fuzzy model
KW - Objective probability
KW - Precedent model
KW - Subjective probability
UR - http://www.scopus.com/inward/record.url?scp=85117075418&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86855-0_11
DO - 10.1007/978-3-030-86855-0_11
M3 - Conference contribution
AN - SCOPUS:85117075418
SN - 9783030868543
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 165
BT - Artificial Intelligence - 19th Russian Conference, RCAI 2021, Proceedings
A2 - Kovalev, Sergei M.
A2 - Kuznetsov, Sergei O.
A2 - Panov, Aleksandr I.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Russian Conference on Artificial Intelligence, RCAI 2021
Y2 - 11 October 2021 through 16 October 2021
ER -
ID: 34603879