26 lines
1.3 KiB
TeX
26 lines
1.3 KiB
TeX
\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}
|
||
|