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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), PROSÂ 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
Annual Review of Computer Science – Annual Reviews
Published: Jun 1, 1990
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