Linear Temporal Public Announcement Logic: a new perspective for
reasoning the knowledge of multi-classifiers
Abstract
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.