Abstract: "In the early years of the research into plausible reasoning several quasi-probabilistic models for handling uncertainty in rule-based expert systems have been proposed. These models were computationally feasible but could not be justified mathematically. Although current research in...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Gaag, Linda C. van der 1959- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Amsterdam 1988
Schriftenreihe:	Centrum voor Wiskunde en Informatica <Amsterdam> / Department of Computer Science: Report CS 88,47
Schlagworte:	Expert systems (Computer science) Probabilities Reasoning
Zusammenfassung:	Abstract: "In the early years of the research into plausible reasoning several quasi-probabilistic models for handling uncertainty in rule-based expert systems have been proposed. These models were computationally feasible but could not be justified mathematically. Although current research in this subarea of artificial intelligence concentrates on the development of mathematically sound models, the early quasi-probabilistic models are still employed frequently in present-day rule-based expert systems. In this paper we show that two of these models, the certainty factor model developed by E.H. Shortliffe and B.G. Buchanan and the subjective Bayesian method developed by R.O. Duda, P.E. Hart and N.J. Nilsson, model different notions of uncertainty. We furthermore discuss the difference in the interpretation and application of production rules in the respective models."
Beschreibung:	11 S.