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Reasoning Under Uncertainty

Reasoning Under Uncertainty Overview One can hardly identify a field in artificial intelligence (AI) that doesn't use some sort of uncertain reasoning, namely, processes leading from evidence or clues to guesses and conclusions under conditions of partial information. Many powerful programs have been written that embody practical solutions to various aspects of reasoning with uncertainty. These include MYCIN (Shortliffe 1 976), INTERNIST (Miller et aI1 982), PROS­ PECTOR (Duda et al 1976), M EDAS (Ben-Bassat et al 1980), RUM (Bonissone et al 1 987), MUM (Cohen et al 1 987a), MDX (Chandrasakaran & M ittaI1983), and MUNIN (Andreassen et al1987). This survey focuses 37 8756--- 70 1 6/90/ 1 1 15-0037$02.00 PEARL OUTIJNE 1. NEED AND DIFFICULTY OF MANAGING UNCERTAINTY 2. EXTENSIONAL VS. INTENSIONAL APPROACHES Computationally attractive Semantically sloppy Semantically clear Computationally clumsy L 3. RIGH1WARD -- _ - J intensional DEVELOPMENTS 4. LEFIWARD DEVELOPMS'\1TS (Belief networks) 5. MEETING GROUNDS? Figure 1 Outline of survey and relationships between extensional and approaches to uncertainty. on a select set of issues, trends, and principles that have emerged from these past works. I hope to describe these in a unifying perspective and in greater depth than a more general survey would permit. For broader http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Computer Science Annual Reviews

Reasoning Under Uncertainty

Annual Review of Computer Science , Volume 4 (1) – Jun 1, 1990

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References (34)

Publisher
Annual Reviews
Copyright
Copyright 1990 Annual Reviews. All rights reserved
Subject
Review Articles
ISSN
8756-7016
DOI
10.1146/annurev.cs.04.060190.000345
Publisher site
See Article on Publisher Site

Abstract

Overview One can hardly identify a field in artificial intelligence (AI) that doesn't use some sort of uncertain reasoning, namely, processes leading from evidence or clues to guesses and conclusions under conditions of partial information. Many powerful programs have been written that embody practical solutions to various aspects of reasoning with uncertainty. These include MYCIN (Shortliffe 1 976), INTERNIST (Miller et aI1 982), PROS­ PECTOR (Duda et al 1976), M EDAS (Ben-Bassat et al 1980), RUM (Bonissone et al 1 987), MUM (Cohen et al 1 987a), MDX (Chandrasakaran & M ittaI1983), and MUNIN (Andreassen et al1987). This survey focuses 37 8756--- 70 1 6/90/ 1 1 15-0037$02.00 PEARL OUTIJNE 1. NEED AND DIFFICULTY OF MANAGING UNCERTAINTY 2. EXTENSIONAL VS. INTENSIONAL APPROACHES Computationally attractive Semantically sloppy Semantically clear Computationally clumsy L 3. RIGH1WARD -- _ - J intensional DEVELOPMENTS 4. LEFIWARD DEVELOPMS'\1TS (Belief networks) 5. MEETING GROUNDS? Figure 1 Outline of survey and relationships between extensional and approaches to uncertainty. on a select set of issues, trends, and principles that have emerged from these past works. I hope to describe these in a unifying perspective and in greater depth than a more general survey would permit. For broader

Journal

Annual Review of Computer ScienceAnnual Reviews

Published: Jun 1, 1990

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