Handling risk attitudes for preference learning and intelligent decision support
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | Modeling Decisions for Artificial Intelligence : 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings |
Redaktører | Vicenc Torra, Torra Narukawa |
Antal sider | 12 |
Forlag | Springer Publishing Company |
Publikationsdato | 2015 |
Sider | 78-89 |
ISBN (Trykt) | 978-3-319-23239-3 |
ISBN (Elektronisk) | 978-3-319-23240-9 |
DOI | |
Status | Udgivet - 2015 |
Navn | Lecture notes in computer science |
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Vol/bind | 9321 |
ISSN | 0302-9743 |
ID: 162454240