Representation of Preferences in Decision-Support Systems

From Tetherless World Wiki

Jump to: navigation, search

Citation: Brad R. Farr and Ross D. Shachter. (1991) Representation of Preferences in Decision-Support Systems. In KSL-91-20, 1991.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Brad R. Farr and Ross D. Shachter
title Representation of Preferences in Decision-Support Systems
number KSL-91-20
institution Knowledge Systems, AI Laboratory
address Washington, D.C
year 1991
Bibtex more
Access Paper
abstract The recommendations of computer-based decision-support systems depend on the preferences of the expert who is responsible for the decisions. Often, these preferences are only represented implicitly, rather than explicitly, in the system. Decision-theoretic preference models that explicitly represent the preferences of the decision maker provide numerous advantages for decision-support systems. In this paper, we describe these advantages. The creation and refinement of decision-theoretic preference models, however, is a difficult task.We describe an accurate and efficient method for determining the preferences of domain experts and refining the model that captures those preferences. In this preference assessment method, we simulate familiar decisions in the expert’s area of expertise. We then infer the preferences of the expert from the choices that the expert makes on the simulated decisions, and use the preference information to refine the model automatically.

KSL Technical Report ID: KSL-91-20
Facts about Representation of Preferences in Decision-Support SystemsRDF feed
Abstract The recommendations of computer-based deci … The recommendations of computer-based decision-support systems depend on the preferences of the expert who is responsible for the decisions. Often, these preferences are only represented implicitly, rather than explicitly, in the system. Decision-theoretic preference models that explicitly represent the preferences of the decision maker provide numerous advantages for decision-support systems. In this paper, we describe these advantages. The creation and refinement of decision-theoretic preference models, however, is a difficult task.We describe an accurate and efficient method for determining the preferences of domain experts and refining the model that captures those preferences. In this preference assessment method, we simulate familiar decisions in the expert’s area of expertise. We then infer the preferences of the expert from the choices that the expert makes on the simulated decisions, and use the preference information to refine the model automatically. rmation to refine the model automatically.
Address Washington, D.C  +
Author Brad R. Farr and Ross D. Shachter  +
Bibtype techreport  +
Has author Brad R. Farr and Ross D. Shachter  +
Has identifier KSL-91-20  +
Has publishing details 1991  +
Has title Representation of Preferences in Decision-Support Systems  +
Has where published KSL-91-20  +
Has year 1991  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-91-20  +
Number KSL-91-20  +
Process note YES  +
Title Representation of Preferences in Decision-Support Systems  +
Year 1991  +
Retrieved from "http://tw.rpi.edu/wiki/Representation_of_Preferences_in_Decision-Support_Systems"
Personal tools