Skip to main content

Uncertainty in Artificial Intelligence

Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, University of Washington, Seattle, July 29-31, 1994

  • 1st Edition - July 1, 1994
  • Author: MKP
  • Language: English
  • Paperback ISBN:
    9 7 8 - 1 - 5 5 8 6 0 - 3 3 2 - 5
  • eBook ISBN:
    9 7 8 - 1 - 4 8 3 2 - 9 8 6 0 - 3

Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in… Read more

Uncertainty in Artificial Intelligence

Purchase options

LIMITED OFFER

Save 50% on book bundles

Immediately download your ebook while waiting for your print delivery. No promo code needed.

Image of books

Institutional subscription on ScienceDirect

Request a sales quote
Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994) covers the papers accepted for presentation at the Tenth Annual Conference on Uncertainty in Artificial Intelligence, held in Seattle, Washington on July 29-31, 1994. The book focuses on the processes, methodologies, and approaches involved in artificial intelligence, including approximations, computational methods, Bayesian networks, and probabilistic inference. The selection first offers information on ending-based strategies for part-of-speech tagging; an evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets; and probabilistic constraint satisfaction with non-Gaussian noise. The text then examines Laplace's method approximations for probabilistic inference in belief networks with continuous variables; computational methods, bounds, and applications of counterfactual probabilities; and approximation algorithms for the loop cutset problem. The book takes a look at learning in multi-level stochastic games with delayed information; properties of Bayesian belief network learning algorithms; and the relation between kappa calculus and probabilistic reasoning. The manuscript also elaborates on intercausal independence and heterogeneous factorization; evidential reasoning with conditional belief functions; and state-space abstraction for anytime evaluation of probabilistic networks. The selection is a valuable reference for researches interested in artificial intelligence.