Handling risk attitudes for preference learning and intelligent decision support
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Handling risk attitudes for preference learning and intelligent decision support. / Franco de los Ríos, Camilo; Hougaard, Jens Leth; Nielsen, Kurt.
Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings. red. / Vicenc Torra; Torra Narukawa. Springer Publishing Company, 2015. s. 78-89 (Lecture notes in computer science, Bind 9321).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Handling risk attitudes for preference learning and intelligent decision support
AU - Franco de los Ríos, Camilo
AU - Hougaard, Jens Leth
AU - Nielsen, Kurt
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-23240-9_7
DO - 10.1007/978-3-319-23240-9_7
M3 - Article in proceedings
SN - 978-3-319-23239-3
T3 - Lecture notes in computer science
SP - 78
EP - 89
BT - Modeling Decisions for Artificial Intelligence
A2 - Torra, Vicenc
A2 - Narukawa, Torra
PB - Springer Publishing Company
ER -
ID: 162454240