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+\begin{abstract}
+
+Policy and industry discourse often reduce artificial intelligence
+(AI) to machine learning framed as “supervised, unsupervised, or
+reinforcement learning.”  This triad omits long-standing AI traditions
+(symbolic expert systems, search \& planning, probabilistic inference,
+control/estimation, and evolutionary/collective computation). We
+formalize the \emph{Operational-Premise Taxonomy}~(OPT), classifying
+AI by its dominant computational mechanism: \Lrn, \Evo, \Sym, \Prb,
+\Sch, \Ctl, and \Swm. For each class we provide core mathematical
+operators, link them to canonical biological mechanisms, and survey
+hybrid compositions. We argue that OPT yields a principled,
+biologically grounded, and governance-usable taxonomy that avoids
+category errors inherent in training-signal–based labels, while
+remaining compact and readable with a short, compositional naming
+code. We propose a grammar for decription, evaluation process methods,
+and argue for the utility of OPT in both post-hoc classification of
+systems and as a design-time adjunct that aids in software development
+and lifecycle risk management processes. OPT can serve as an adjunct
+and complement to existing frameworks of software risk management
+given its basis in the operational mechanisms of the software rather
+than more superficial characteristics commonly used.
+
+\end{abstract}
+