2010
Ribeiro, J.; Abelha, A.; Machado, J.; Marques, A.; Neves, J.
The inference process with quality evaluation in healthcare environments Proceedings Article
Em: pp. 183-188, Yamagata, 2010, ISBN: 9780769541471, (cited By 1; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351).
Resumo | Links | BibTeX | Etiquetas: Computer software selection and evaluation; Decision making; Decision support systems; Decision theory; Information science; Intelligent systems; Knowledge representation; Logic programming, Quality control
@inproceedings{Ribeiro2010183,
title = {The inference process with quality evaluation in healthcare environments},
author = {J. Ribeiro and A. Abelha and J. Machado and A. Marques and J. Neves},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649268622&doi=10.1109%2fICIS.2010.160&partnerID=40&md5=ea6e75d4229d65da60c4f998202906a8},
doi = {10.1109/ICIS.2010.160},
isbn = {9780769541471},
year = {2010},
date = {2010-01-01},
journal = {Proceedings - 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010},
pages = {183-188},
address = {Yamagata},
abstract = {Intelligent Systems require the ability to reason with incomplete information, because in the real world complete information is hard to obtain, even in the most controlled situation. In recent years, many formalisms have been proposed tacking the matter of uncertain, incomplete in logic programs and databases. However, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes. The evaluation of knowledge that stems out from logic programs becomes a point of research. The Quality-of-Information concept demonstrated their applicability in many dynamic environments and for decision making purposes. In this paper we present an illustrative example of the inference process in decisions in healthcare environments. Under the Extended Logic Programming paradigm to knowledge representation and reasoning, we present the evolutive perspective of the inference process to achieve logical programs (or theories) corresponding to the best theorems to solve a problem or take a decision. For the evaluation of the best theories we use a quantification of the quality-of-information that stems out from a logic program. © 2010 IEEE.},
note = {cited By 1; Conference of 9th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2010 ; Conference Date: 18 August 2010 Through 20 August 2010; Conference Code:82351},
keywords = {Computer software selection and evaluation; Decision making; Decision support systems; Decision theory; Information science; Intelligent systems; Knowledge representation; Logic programming, Quality control},
pubstate = {published},
tppubtype = {inproceedings}
}
Intelligent Systems require the ability to reason with incomplete information, because in the real world complete information is hard to obtain, even in the most controlled situation. In recent years, many formalisms have been proposed tacking the matter of uncertain, incomplete in logic programs and databases. However, qualitative models and qualitative reasoning have been around in Artificial Intelligence research for some time, in particular due the growing need to offer support in decision-making processes. The evaluation of knowledge that stems out from logic programs becomes a point of research. The Quality-of-Information concept demonstrated their applicability in many dynamic environments and for decision making purposes. In this paper we present an illustrative example of the inference process in decisions in healthcare environments. Under the Extended Logic Programming paradigm to knowledge representation and reasoning, we present the evolutive perspective of the inference process to achieve logical programs (or theories) corresponding to the best theorems to solve a problem or take a decision. For the evaluation of the best theories we use a quantification of the quality-of-information that stems out from a logic program. © 2010 IEEE.