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Journals ACM SIGACT News Volume 34 Issue 1

In this survey we present partial information classes, which have been studied under different names and in different contexts in the literature. They are defined in terms of partial information algorithms. Such algorithms take a word tuple as input and yield a small set of possibilities for its characteristic string as output. We define a unified framework for the study of partial information classes and show how previous notions fit into the framework. The framework allows us to study the relationship of a large variety of partial information classes in a uniform way. We survey how partial information classes are related to other complexity theoretic notions like advice classes, lowness, bi-immunity, NP-completeness, and decidability.

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Partial information classes

Nickelsen, Arfst; Tantau, Till
ACM SIGACT News , Volume 34 (1)
Association for Computing Machinery – Mar 1, 2003

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