Uncertainty in artificial intelligence/ [1]

✍ Scribed by R. K. Ghatnagar, C. Berenstein, L. N. Kanal, D. Lavine, B. Chandrasekaran, M. C. Tanner, B. P. Wise, M. Henrion et al, Laveen N. Kanal, John F. Lemmer

• Publisher: North-Holland
• Year: 1986
• Tongue: English
• Leaves: 484
• Series: Machine Intelligence and Pattern Recognition 4
• Category: Library

✦ Synopsis

How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy. Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context

✦ Table of Contents

Content: Overviews and Reviews (C. Berenstein, R.K. Bhatnagar, L.N. Kanal and D. Lavine). Explication or Critique of Current Approaches to Uncertainty (P.P. Bonissone, B. Chandrasekaran, P. Cheeseman, K.S. Decker, B.N. Grosof, D. Heckerman, M. Henrion, E. Horvitz, D. Hunter, R.W. Johnson, J.F. Lemmer, G. Shafer, J.E. Shore, D.J. Spiegelhalter, M.C. Tanner, B.P. Wise, L.A. Zadeh and A.C. Zimmer). Synthesis of Current Approaches to Uncertainty (C.Y. Chong, M.S. Cohen, R.M. Fung, G.S.-H. Liu and R.R. Yager). Incorporating Uncertainty in Systems (J. Breese, M.L. Ginsberg, P.D. Holden, S. Holtzman, K.G. Kempf, T.S. Levitt, J. Pearl, B.M. Perrin, R.D. Shachter, D.S. Vaughan and R.M. Yadrick). Techniques for Inducing and Processing Uncertain Information (M. Bauer, N.C. Dalkey, H. Hamburger, S.J. Hanson and L. Rendell). Alternate Perspectives (J. Fox, R.P. Loui and R. Solomonoff). Alternatives to Minimax in Game Playing (B. Abramson, D. Nau, P. Purdom and C.-H. Tzeng).