  Providing accurate/suitable information on behaviors in sma\-rt environments
is a challenging and crucial task in pervasive computing where
context-awareness and pro-activity are of fundamental importance. Behavioral
identifications enable to abstract higher-level concepts that are interesting
to applications. This work proposes the unified logical-based framework to
recognize and analyze behavioral specifications understood as a formal logic
language that avoids ambiguity typical for natural languages. Automatically
discovering behaviors from sensory data streams as formal specifications is of
fundamental importance to build seamless human-computer interactions. Thus, the
knowledge about environment behaviors expressed in terms of temporal logic
formulas constitutes a base for the reactive and precise reasoning processes to
support trustworthy, unambiguous and pro-active decisions for applications that
are smart and context-aware.
