Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
A mission planning architecture for an autonomous vehicle
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Improving inference through conceptual clustering
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
An experimental comparison of knowledge engineering for expert systems and for decision analysis
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Improving inference through conceptual clustering
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
An experimental comparison of knowledge engineering for expert systems and for decision analysis
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
The probability of a possibility: adding uncertainty to default rules
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Belief revision in probability theory
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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In this paper, it is argued that probability theory, when used correctly, is suffrcient for the task of reasoning under uncertainty. Since numerous authors have rejected probability as inadequate for various reasons, the bulk of the paper is aimed at refuting these claims and indicating the scources of error. In particular, the definition of probability as a measure of belief rather than a frequency ratio is advocated, since a frequency interpretation of probability drastically restricts the domain of applicability. Other sources of error include the confusion between relative and absolute probability, the distinction between probability and the uncertainty of that probability. Also, the interaction of logic and probability is discusses and it is argued that many extensions of logic, such as "default logic" are better understood in a probabilistic framework. The main claim of this paper is that the numerous schemes for representing and reasoning about uncertainty that have appeared in the AI literature are unnecessary--probability is all that is needed.