Linear Temporal Public Announcement Logic: a new perspective for
  reasoning the knowledge of multi-classifiers
  Current applied intelligent systems have crucial shortcomings either in
reasoning the gathered knowledge, or representation of comprehensive integrated
information. To address these limitations, we develop a formal transition
system which is applied to the common artificial intelligence (AI) systems, to
reason about the findings. The developed model was created by combining the
Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL), which will
be done to analyze both single-framed data and the following time-series data.
To do this, first, the achieved knowledge by an AI-based system (i.e.,
classifiers) for an individual time-framed data, will be taken, and then, it
would be modeled by a PAL. This leads to developing a unified representation of
knowledge, and the smoothness in the integration of the gathered and external
experiences. Therefore, the model could receive the classifier's predefined --
or any external -- knowledge, to assemble them in a unified manner. Alongside
the PAL, all the timed knowledge changes will be modeled, using a temporal
logic transition system. Later, following by the translation of natural
language questions into the temporal formulas, the satisfaction leads the model
to answer that question. This interpretation integrates the information of the
recognized input data, rules, and knowledge. Finally, we suggest a mechanism to
reduce the investigated paths for the performance improvements, which results
in a partial correction for an object-detection system.
