| Full text | |
| Author(s): |
Kikuti, Daniel
;
Cozman, Fabio Gagliardi
;
Shirota Filho, Ricardo
Total Authors: 3
|
| Document type: | Journal article |
| Source: | ARTIFICIAL INTELLIGENCE; v. 175, n. 7-8, p. 20-pg., 2011-05-01. |
| Abstract | |
This paper presents new insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected utility. We assume that incomparability amongst strategies is caused by indeterminacy/imprecision in probability values. We investigate six criteria for consequentialist strategy selection: Gamma-Maximin, Gamma-Maximax, Gamma-Maximix, Interval Dominance, Maximality and E-admissibility. We focus on the popular decision tree and influence diagram representations. Algorithms resort to linear/multilinear programming; we describe implementation and experiments. (C) 2010 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 08/03995-5 - Logprob: probabilistic logic --- foundations and computational applications |
| Grantee: | Marcelo Finger |
| Support Opportunities: | Research Projects - Thematic Grants |