  This article argues that the Situation theory and the Channel theory can be
used as a general framework for Imperfect Information Management. Different
kinds of imperfections are uncertainty, imprecision, vagueness, incompleteness,
inconsistency, and context-dependency which can be handled pretty well by our
brain. Basic approaches like probability theory and standard logic are
intrinsically inefficient in modeling fallacious minds. The generalized
probability and nonstandard logic theories have epistemological motivations to
provide better models for information integration in cognitive agents. Among
many models of them, possibility theory and probabilistic logic theory are the
best approaches. I argue, based on a review of different approaches to
Imperfect Information Management, that a good framework for it is the Situation
theory of Barwise and the Channel theory of Barwise-Seligman. These theories
have relied on a powerful and unique epistemological-based notion of
information to refer to partiality. These frameworks have a proper approach for
context modeling to handle common knowledge and incomplete information. Also,
they distinguish belief from knowledge clearly to model the non-monotonic and
dynamic nature of knowledge. They discern the logic of the world from
information flow in the mind. The objectification process in these theories
reveals to us the nature of default or probabilistic rules in perceptions. The
concept of the channel can be used to represent those types of reasoning
mechanisms that move from one model or logic to another one. The imprecision in
our perceptions causes fuzziness in reasoning and vagueness in communication
that can be represented by some suitable classifications connected by some
channels. This new framework like a network framework can provide a scalable
and open framework to cover different models based on a relativistic notion of
truth.
