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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14340
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| Title: | A Comparative Analysis of the Empirical Validity of Two Rule Based
Belief Languages |
| Authors: | Schocken, Shimon
Wang, Yu-Ming |
| Keywords: | Belief revision
inexact reasoning
certainty factors
uncertainty in artificial intelligence |
| Issue Date: | 31-Jul-1992 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-92-24 |
| Abstract: | Rule based expert systems deal with inexact reasoning through a variety
of quasi-probabilistic methods, including the widely used certainty
factors (CF) and subjective Bayesian (SB) models, versions of which are
implemented in many commercial expert system shells. Previous research
established that under certain independence assumptions, SB and CF are
ordinally compatible: when used to compute the beliefs in several
hypotheses of interest under the same set of circumstances, the
hypothesis that will attain the highest posterior probability will also
attain the highest certainty factor, etc. This is very relevant to the
expert systems field, where most inference engines and explanation
facilities are designed to utilize the relative scales of posterior
beliefs, making little or no use of their absolute magnitudes. The
objective of this research is to explore empirically whether the
compatibility of SB and CF extends to the field, where subjective
degrees of belief and different elicitation procedures might bias the
mathematical kinship of the two belief languages. In particular, we seek
to know (i) whether this bias is random or systematic; and (ii) what the
bias reveals about SB and CF as two alternative means to elicit and
revise beliefs in a rule based system. |
| URI: | http://hdl.handle.net/2451/14340 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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