During the past decades, many methods have been developed for the creation of knowledge-based systems (KBS). For these methods, probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelation. KAMET is a model-based methodology designed to manage knowledge acquisition from multiple knowledge sources by Cairo, O. (1998) that leads to a graphical model that represents causal relations. Up to now, all inference methods developed for these models are rule-based, and therefore eliminate most of the probabilistic information. We present a way to combine the benefits of Bayesian networks and KAMET, and reduce their problems. To achieve this, we show a transformation that generates directed acyclic graphs, the basic structure of Bayesian networks by Castillo, E., Gutierrez J.M. and Hadi A.S. (1997), and conditional probability tables, from KAMET models. Thus, inference methods for probabilistic networks may be used in KAMET models

Cairó, O., Penaloza, R. (2003). Using Bayesian Networks as an Inference Engine in KAMET. In SCCC '03: Proceedings of the XXIII International Conference of the Chilean Computer Science Society (pp.79-85). IEEE-Press [10.1109/SCCC.2003.1245448].

Using Bayesian Networks as an Inference Engine in KAMET

Penaloza, R
2003

Abstract

During the past decades, many methods have been developed for the creation of knowledge-based systems (KBS). For these methods, probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelation. KAMET is a model-based methodology designed to manage knowledge acquisition from multiple knowledge sources by Cairo, O. (1998) that leads to a graphical model that represents causal relations. Up to now, all inference methods developed for these models are rule-based, and therefore eliminate most of the probabilistic information. We present a way to combine the benefits of Bayesian networks and KAMET, and reduce their problems. To achieve this, we show a transformation that generates directed acyclic graphs, the basic structure of Bayesian networks by Castillo, E., Gutierrez J.M. and Hadi A.S. (1997), and conditional probability tables, from KAMET models. Thus, inference methods for probabilistic networks may be used in KAMET models
Si
paper
reasoning; Bayesian networks; knowledge representation
English
International Conference of the Chilean Computer Science Society, SCCC 2003 6-7 November
0-7695-2008-1
Cairó, O., Penaloza, R. (2003). Using Bayesian Networks as an Inference Engine in KAMET. In SCCC '03: Proceedings of the XXIII International Conference of the Chilean Computer Science Society (pp.79-85). IEEE-Press [10.1109/SCCC.2003.1245448].
Cairó, O; Penaloza, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/233774
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