A deterministic algorithm for solving imprecise decision problems
Abstract
Today there are numerous tools for decision analysis, suitable
both for human and artificial decision makers. Most of these tools
require the decision maker to provide precise numerical estimates of
probabilities and utilities. Furthermore, they lack the capability to
handle inconsistency in the decision models, and will fail to deliver
an answer unless the formulation of the decision problem is
consistent. In this paper we present an algorithm for evaluating
imprecise decision problems expressed using belief distributions, that
also can handle inconsistency in the model. The same algorithm can be
applied to decision models where probabilities and utilities are given
as intervals or point values, which gives us a general method for
evaluating inconsistent decision models with varying degree of
expressiveness.
Sample citation
Håkan L. S. Younes and
Love Ekenberg. 2000.
A deterministic algorithm for solving imprecise decision problems. In
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference, edited by Jim Etheredge and Bill Manaris, 313–317, Orlando, Florida. AAAI Press.
Full paper (5 pages, 19 references)
Copyright © 2000, American Association for Artificial Intelligence. All rights reserved.
Presentation (30 slides)