Manual Uncertainty in Artificial Intelligence. Proceedings of the Ninth Conference (1993)

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Please review our Terms and Conditions of Use and check box below to share full-text version of article. Abstract We present the syntax and proof theory of a logic of argumentation, LA. Citing Literature. Volume 11 , Issue 1 February Pages Related Information. Close Figure Viewer. Browse All Figures Return to Figure.

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Thomas Dean’s Publications

Old Password. Boutilier, R. Brafman, C. O'Rorke Ed. This book was never published, but here is a draft of the paper I wrote. Andrew Csinger, Kelly S. Nevin Lianwen Zhang , R. Gabbay, C. Hogger J.

Robinson eds. David Poole and K.

Yang Xiang , David Poole and M. Yang Xiang , B. Pant, A. Eisen, M. Zhang, R. Lauderdale, Florida, pages , January Xiang, David Poole and M. Republished in R.

Qualitative relationships

Brachman, H. Levesque and R. Reiter Eds. Xiang, B. David Poole and G. Reprinted in W. Hamscher, L. Console and J. Helft, K. Bonissone, M. With Bolt, they introduced influences capturing the prior effect of a generally non-monotonic influence. More in general, information about signs in different contexts is captured by using context-specific signs. For details see: S. Decision making in qualitative influence diagrams. Exploiting non-monotonic influences in qualitative belief networks. Bolt, S.


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Context-specific sign-propagation in qualitative probabilistic networks. Artificial Intelligence, vol. A notion of strength When trade-offs are modelled in a qualitative network, inference will give ambiguous and thus uninformative results. To overcome this problem, at least ot some extent, different approaches for introducing a notion of strength into QPNs have been proposed, based on either absolute or relative orders of magnitudes.

Enhancing QPNs for trade-off resolution.

Thomas Dean’s Publications

Renooij, S. Parsons, P. Pardieck Applications for qualitative probabilistic networks Probability elicitation In constructing probabilistic networks, especially when doing this by hand, knowledge acquisition can be facilitated by taking advantage of easily acquired qualitative information and by not requiring exact values. Qualitative probabilistic relations can therefore be elicited prior to or as part of the probability elicitation process.

The qualitative relations can be used either as constraints on the probability distributions to be assessed, or for studying the reasoning behaviour of the quantitative network under construction. See e.

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Druzdzel, L. Elicitation of probabilities for belief networks: combining qualitative and quantitative information. Morgan Kaufmann Publishers, San Francisco, pp.


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From qualitative to quantitative probabilistic networks. To facilitate probability elicitation, often the interactions between variables are assumed to correspond to a noisy-or. Different authors have discussed the relationship between the noisy-or and the different qualitative relations; some also discuss Occam's razor.

Thomas Dean’s Publications

Agosta Conditional inter-causally independent node distributions, a property of noisy-or models. Explanation Druzdzel used QPNs to generate verbal explanantions of probabilistic reasoning. See M. Qualitative decision making and planning As first introduced by Wellman, QPNs were intended to support qualitative decision making by means of the additive synergy.

Argumentation Parsons studied the relation between QPNs and systems for argumentation. Prakken and Renooij use a QPN to construct legal arguments.