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Handling risk attitudes for preference learning and intelligent decision support

Publikation: Forskning - peer reviewKonferencebidrag i proceedings

Intelligent decision support should allow integrating human knowledge with efficient algorithms for making interpretable and useful recommendations on real world decision problems. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. Here, risk attitudes are represented by means of fuzzy-linguistic structures, and an interactive methodology is proposed for learning preferences from a group of decision makers (DMs). The methodology is built on a multi-criteria framework allowing imprecise observations/measurements, where DMs reveal their attitudes in linguistic form and receive from the system their associated type, characterized by a preference order of the alternatives, together with the amount of consensus and dissention existing among the group. Following on the system's feedback, DMs can negotiate on a common attitude while searching for a satisfactory decision.
OriginalsprogEngelsk
TitelModeling Decisions for Artificial Intelligence : 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings
RedaktørerVicenc Torra, Torra Narukawa
Antal sider12
ForlagSpringer Publishing Company
Publikationsdato2015
Sider78-89
ISBN (Trykt)978-3-319-23239-3
ISBN (Elektronisk)978-3-319-23240-9
DOI
StatusUdgivet - 2015
SerieLecture Notes in Computer Science
Vol/bind9321
ISSN0302-9743

ID: 162454240