  Underlying computational model has an important role in any computation. The
state and transition (such as in automata) and rule and value (such as in Lisp
and logic programming) are two comparable and counterpart computational models.
Both of deductive and model checking verification techniques are relying on a
notion of state and as a result, their underlying computational models are
state dependent. Some verification problems (such as compliance checking by
which an under compliance system is verified against some regulations and
rules) have not a strong notion of state nor transition. Behalf of it, these
systems have a strong notion of value symbols and declarative rules defined on
them. SARV (Stateless And Rule-Based Verification) is a verification framework
that designed to simplify the overall process of verification for stateless and
rule-based verification problems (e.g. compliance checking). In this paper, a
formal logic-based framework for creating intelligent compliance checking
systems is presented. We define and introduce this framework, report a case
study and present results of an experiment on it. The case study is about
protocol compliance checking for smart cities. Using this solution, a Rescue
Scenario use case and its compliance checking are sketched and modeled. An
automation engine for and a compliance solution with SARV are introduced. Based
on 300 data experiments, the SARV-based compliance solution outperforms famous
machine learning methods on a 3125-records software quality dataset.

Software Engineering;Artificial Intelligence;Logic in Computer Science