Shai Fine, Yishay Mansour
Machine Learning
The modal logic LL was introduced by Halpern and Rabin as a means of doing qualitative reasoning about likelihood. Here the relationship between LL and probability theory is examined. It is shown that there is a way of translating probability assertions into LL in a sound manner, so that LL in some sense can capture the probabilistic interpretation of likelihood. However, the translation is subtle; several more obvious attempts are shown to lead to inconsistencies. We also extend LL by adding modal operators for knowledge. This allows us to reason about the interaction between knowledge and likelihood. The propositional version of the resulting logic LLK is shown to have a complete axiomatization and to be decidable in exponential time, provably the best possible. Copyright © 1989, Wiley Blackwell. All rights reserved
Shai Fine, Yishay Mansour
Machine Learning
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