Uncertainty in Artificial Intelligence : Proveedings of the 7th Conference

D'Ambrosio

ISBN 10: 1558602038 / ISBN 13: 9781558602038

Edité par Morgan Kaufmann Publishers, 1998

A propos de cet article

The text is clean with no remarks or highlights. 406 pages with no index. Full Size Paperbackbooks are NOT signed. We will state signed at the description section. we confirm they are signed via email or stated in the description box. - Specializing in academic, collectiblle and historically significant, providing the utmost quality and customer service satisfaction. For any questions feel free to email us.

N° de réf. du vendeur 5000

Détails bibliographiques

• Titre: Uncertainty in Artificial Intelligence : Proveedings of the 7th Conference
• Éditeur: Morgan Kaufmann Publishers
• Date de parution: 1998
• Langue: anglais
• ISBN à 10 chiffres: 1558602038
• ISBN à 13 chiffres: 9781558602038
• Reliure: Paperback
• État de l'article: Fine
• Catalogues du vendeur:
  • HOBBIES & ANTIQUES

A propos de ce titre

Synopsis

Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference (1991) covers the papers presented at the Seventh Conference on Uncertainty in Artificial Intelligence, held on July 13-15, 1991 at the University of California at Los Angeles (UCLA). The book focuses on the processes, technologies, developments, and approaches involved in artificial intelligence. The selection first offers information on combining multiple-valued logics in modular expert systems; constraint propagation with imprecise conditional probabilities; and Bayesian networks applied to therapy monitoring. The text then examines some properties of plausible reasoning; theory refinement on Bayesian networks; combination of upper and lower probabilities; and a probabilistic analysis of marker-passing techniques for plan-recognition. The publication ponders on symbolic probabilistic inference (SPI) with continuous variables, SPI with evidence potential, and local expression languages for probabilistic dependence. Topics include local expression languages for probabilistic knowledge, evidence potential algorithm, symbolic inference with evidence potential, and SPI with continuous variables algorithm. The manuscript also takes a look at the compatibility of quantitative and qualitative representations of belief and a method for integrating utility analysis into an expert system for design evaluation under uncertainty. The selection is a valuable source of data for researchers interested in artificial intelligence.