
Justifying the Principle of Interval Constraints
When knowledge is obtained from a database, it is only possible to deduc...
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Revisable Justified Belief: Preliminary Report
The theory CDL of Conditional Doxastic Logic is the singleagent version...
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Relations among conditional probabilities
We describe a Groebner basis of relations among conditional probabilitie...
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Some Extensions of Probabilistic Logic
In [12], Nilsson proposed the probabilistic logic in which the truth val...
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An Implementation of a Method for Computing the Uncertainty in Inferred Probabilities in Belief Networks
In recent years the belief network has been used increasingly to model s...
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A Probabilistic Calculus of Actions
We present a symbolic machinery that admits both probabilistic and causa...
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Approximate Deduction in Single Evidential Bodies
Results on approximate deduction in the context of the calculus of evidence of DempsterShafer and the theory of interval probabilities are reported. Approximate conditional knowledge about the truth of conditional propositions was assumed available and expressed as sets of possible values (actually numeric intervals) of conditional probabilities. Under different interpretations of this conditional knowledge, several formulas were produced to integrate unconditioned estimates (assumed given as sets of possible values of unconditioned probabilities) with conditional estimates. These formulas are discussed together with the computational characteristics of the methods derived from them. Of particular importance is one such evidence integration formulation, produced under a belief oriented interpretation, which incorporates both modus ponens and modus tollens inferential mechanisms, allows integration of conditioned and unconditioned knowledge without resorting to iterative or sequential approximations, and produces elementary mass distributions as outputs using similar distributions as inputs.
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