M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, 94:43--54, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....about the values of the two hidden variables. As this short description indicates, such a structure is interpretable in that it represents the underlying (theoretical) assumption. Beside an interpretation of the structure itself by the experienced user, an explanation component for BNs (see, e.g. [11]) can generate verbal statements to justify its decisions, like An increase in time pressure leads with high probability to an increase in the actual cognitive load which in turn leads to a smaller number of syllables produced by the experimental subjects. A structure without these hidden ....

Druzdzel, M.J.: Qualitative verbal explanations in Bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly 94 (1996) 43--54

....with numerical probabilities may be uncomfortable. Researchers have recognised the importance of providing users with more easily understandable explanations of the results, for which numbers may not necessarily be the best option and verbal probability expressions are seen as good alternatives [16,13]. Except in situations where the odds are objectively measurable, most people feel more at ease with verbal probability expressions than with numbers. When people communicate probabilities, they frequently do so in words rather than in numbers. In the development of a computer system, viz. a ....

M.J. Druzdzel. Qualitative verbal explanations in Bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, special issue on Bayesian belief networks, 94:43 -- 54, 1996.

....interaction nor any possibility of adaptation. In principle, these explanations do not require that the user is familiar with probabilistic reasoning, although knowledge on the methods involved will certainly help to understand the explanations. 3.3. 5 Qualitative reasoning Druzdzel and Henrion [18, 21, 22, 19, 31] also proposed another explanation method based on the transformation of a causal Bayesian network into a qualitative probabilistic network (QPN) 55, 56] in which the relation between two adjacent nodes is denoted as positive ( negative ( null (0) or unknown ( there also relations that ....

M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Articial Intelligence and Simulation of Behaviour Quarterly, 94:4354, 1996. Special Issue on Bayesian Belief Networks.

....with numerical probabilities may be uncomfortable. Researchers have recognised the importance of providing users with more easily understandable explanations of the results, for which numbers may not necessarily be the best option and verbal probability expressions are seen as good alternatives [9, 10]. Except in situations where the odds are objectively measurable, most people feel more at ease with verbal probability expressions than with numbers. When people communicate probabilities, they frequently do so in words rather than in numbers. In the development of a Renooij Witteman 4 ....

Druzdzel, M.J., Qualitative verbal explanations in Bayesian belief networks, AI and Simulation of Behaviour Quarterly (special issue on Bayesian belief networks) 94, 43-54, 1996.

....system builders now commonly use belief networks. Important applications include forecasting, risk assessment, classification, and decision making. Using belief network models in expert system applications requires appropriate explanation facilities. Chamberlain and Nordahl (1988) Cooper (1989) Druzdzel (1996), Henrion and Druzdzel (1990) Lauritzen and Spiegelhalter (1988) Pearl (1987) Suermondt (1991) and Suermondt and Cooper (1993) and have all made suggestions as to how such explanations might be generated. These approaches, however, fail to fully exploit the natural visual metaphor of the ....

....that verbal explanations might be generated from a belief network. For example: Disease A provides a fairly strong explanation for indicant E but provides only a partial explanation for indicant C. Indicant B, if it were present, would provide almost conclusive evidence for A. Cooper (1989) and Druzdzel (1996) discuss similar approaches. Such explanations would be desirable in many situations. We believe that they could be derived from the quantitative graphical approach we propose, combined with pre stored text. Henrion and Druzdzel (1990) consider explanation in the context of directed graphs and ....

Druzdzel, M.K. (1996). Qualitative verbal explanations in Bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, to appear.

....Milwaukee WI 53201 Tel: 414 229 4955 Fax: 414 229 6958 y Section of Information and Decision Sciences Department of Radiology Medical College of Wisconsin Milwaukee, WI 53226 Tel: 414 259 2173 Fax: 414 259 9290 z Introl Corp. 301 N. Water Street Milwaukee, WI 53202 Tel: 273 6100 September 15, 1996 To appear in Artificial Intelligence in Medicine. Abstract We present an educational tool for bringing the information contained in a Bayesian network to the end user in an easily intelligible form. The banter shell is designed to tutor users in evaluation of hypotheses and selection of ....

M.J. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Artificial Intelligence and Simulation of Behavior Quarterly, 94:43--54, April 1996.

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Druzdzel, Marek J. 1996. Qualitative verbal explanations in Bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly 94:43--54.

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Marek J. Druzdzel. Qualitative Verbal Explanations in Bayesian Belief Networks. To appear in Artificial Intelligence and Simulation of Behaviour Quarterly, special issue on belief networks, 1996.

....into a qualitative explanation method. This is the foundation of qualitative belief propagation based explanations [17] Qualitative belief propagation traces the signs of change in probability on the paths from the evidence to nodes of interest. These signs are then translated into verbal form [8, 10, 17]. Qualitative explanations can be generated by extracting qualitative properties of a fully specified quantitative network and running an extremely efficient algorithm that determines the signs of changes in the network [11] Suermondt [26] provides a thorough quantitative treatment of several ....

Marek J. Druzdzel. Qualitative verbal explanations in Bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, 94:43--54, 1996.

No context found.

M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, 94:43--54, 1996.

No context found.

M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, 94:43--54, 1996.

No context found.

M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. Artificial Intelligence and Simulation of Behaviour Quarterly, 94:43--54, 1996.

No context found.

M. Druzdzel. Qualitative verbal explanations in bayesian belief networks. AI and Simulation of Behaviour Quarterly (special issue on Bayesian belief networks), 94:43--54, 1996.

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