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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dubois, D. Prade, H. Smets, P. |
| Copyright Year | 1996 |
| Abstract | This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example. |
| Sponsorship | IEEE Systems, Man, and Cybernetics Society |
| Starting Page | 361 |
| Ending Page | 377 |
| Page Count | 17 |
| File Size | 2331311 |
| File Format | |
| ISSN | 10834427 |
| Volume Number | 26 |
| Issue Number | 3 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1996-05-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Artificial intelligence Uncertainty Knowledge representation Decision making Prototypes Logic Computer languages Medical expert systems Diagnostic expert systems |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software |
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