# The relationship between knowledge, belief, and certainty

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## Abstract

We consider the relation between knowledge and certainty, where a fact is*known* if it is true at all worlds an agent considers possible and is*certain* if it holds with probability 1. We identify certainty with belief, interpreted probabilistically. We show that if we assume one fixed probability assignment, then the logic KD45, which has been identified as perhaps the most appropriate for belief, provides a complete axiomatization for reasoning about certainty. Just as an agent may believe a fact although*ϕ* is false, he may be certain that a fact*ϕ* is true although*ϕ* is false. However, it is easy to see that an agent can have such false (probabilistic) beliefs only at a set of worlds of probability 0. If we restrict attention to structures where all worlds have positive probability, then S5 provides a complete axiomatization. If we consider a more general setting, where there might be a different probability assignment at each world, then by placing appropriate conditions on the*support* of the probability function (the set of worlds which have non-zero probability), we can capture many other well-known modal logics, such as T and S4. Finally, we consider*Miller's principle*, a well-known principle relating higher-order probabilities to lower-order probabilities, and show that in a precise sense KD45 characterizes certainty in those structures satisfying Miller's principle.

## Keywords

Neural Network Artificial Intelligence Complex System General Setting Nonlinear Dynamics## Preview

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