Qualitative Discovery in Medical Databases
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
The characteristic error approach to conflict resolution
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Intelligent market based learner modeling
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Conditions for the existence of belief functions corresponding to intervals of belief
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Evidential reasoning in a categorial perspective: conjunction and disjunction of belief functions
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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This paper introduces a formal method for integrating knowledge derived from a variety of sources for use in "perceptual reasoning". The formalism is based on the "evidential proposltlonal calculus", a derivative of Shafer's mathematical theory of evidence [4]. It is more general than either a Boolean or Bayeslan approach, providing for Boolean and Bayeslan inferencing when the appropriate information is available. In this formalism, the likelihood of a proposition A is represented as a subinterval, [s(A), p(A)], of the unit interval, [0, 1]. The evidential support for proposition A is represented by s(A), while p(A) represents its degree of plausibility; p(A) can also be interpreted as the degree to which one fails to doubt A, p(A) being equal to one minus the evidential support for A. This paper describes how evidential information, furnished by a knowledge source in the form of a probability "mass" distribution, can be converted to this interval representation; how, through a set of inference rules for computing intervals of dependent propositions, this Information can be extrapolated from those propositions it directly bears upon, to those it indirectly bears upon; and how multiple bodies of evidential Information can be pooled. A sample application of this approach, modeling the operation of a collection of sensors (a particular type of knowledge source), illustrates these techniques.