Handling risk attitudes for preference learning and intelligent decision support

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

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. ed. / Vicenc Torra; Torra Narukawa. Springer Publishing Company, 2015. p. 78-89 (Lecture notes in computer science, Vol. 9321).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Franco de los Ríos, C, Hougaard, JL & Nielsen, K 2015, Handling risk attitudes for preference learning and intelligent decision support. in V Torra & T Narukawa (eds), Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings. Springer Publishing Company, Lecture notes in computer science, vol. 9321, pp. 78-89. https://doi.org/10.1007/978-3-319-23240-9_7

APA

Franco de los Ríos, C., Hougaard, J. L., & Nielsen, K. (2015). Handling risk attitudes for preference learning and intelligent decision support. In V. Torra, & T. Narukawa (Eds.), Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings (pp. 78-89). Springer Publishing Company. Lecture notes in computer science Vol. 9321 https://doi.org/10.1007/978-3-319-23240-9_7

Vancouver

Franco de los Ríos C, Hougaard JL, Nielsen K. Handling risk attitudes for preference learning and intelligent decision support. In Torra V, Narukawa T, editors, Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings. Springer Publishing Company. 2015. p. 78-89. (Lecture notes in computer science, Vol. 9321). https://doi.org/10.1007/978-3-319-23240-9_7

Author

Franco de los Ríos, Camilo ; Hougaard, Jens Leth ; Nielsen, Kurt. / Handling risk attitudes for preference learning and intelligent decision support. Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings. editor / Vicenc Torra ; Torra Narukawa. Springer Publishing Company, 2015. pp. 78-89 (Lecture notes in computer science, Vol. 9321).

Bibtex

@inproceedings{562b63a601694bfd9333a64f3680ccfe,
title = "Handling risk attitudes for preference learning and intelligent decision support",
abstract = "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. ",
author = "{Franco de los R{\'i}os}, Camilo and Hougaard, {Jens Leth} and Kurt Nielsen",
year = "2015",
doi = "10.1007/978-3-319-23240-9_7",
language = "English",
isbn = "978-3-319-23239-3",
series = "Lecture notes in computer science",
publisher = "Springer Publishing Company",
pages = "78--89",
editor = "Torra, {Vicenc } and Narukawa, {Torra }",
booktitle = "Modeling Decisions for Artificial Intelligence",

}

RIS

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