92 lines
5.2 KiB
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Executable File
92 lines
5.2 KiB
TeX
Executable File
\section{Etymological Glossary of OPT Class Names}
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\label{app:opt-etymology}
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The Operational Premise Taxonomy (OPT) uses short three-letter codes to denote
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fundamental operative mechanisms (\Lrn, \Evo, \Sym, \Prb, \Sch, \Ctl, \Swm).
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For mnemonic and conceptual coherence, each mechanism is also associated with a
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semantically suggestive ``particle-style'' label in \emph{-on}, evoking both a
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unit of behavior and an operative principle (by analogy with terms such as
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``neuron'', ``phonon'', ``boson'', ``fermion''). This appendix summarizes the
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etymological motivations for these labels.
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\begin{description}
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\item[\textbf{Lrn} --- \emph{Learnon}.]
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The mechanism \Lrn~covers parametric learning systems: differentiable models
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with trainable parameters (e.g., neural networks trained by gradient descent,
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linear models with least-squares updates, temporal-difference learning).
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The label \emph{Learnon} combines modern English \emph{learn} with the suffix
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\emph{-on} to denote a basic unit or agent of learning activity. The verb
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\emph{learn} traces back to Old English \emph{leornian}, ``to acquire
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knowledge, to study'', from Proto-Germanic \emph{*liznojan}. \emph{Learnon}
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thus names the operative principle ``that which learns by adjusting its
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internal parameters''.
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\item[\textbf{Evo} --- \emph{Evolon}.]
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The mechanism \Evo~comprises population-based adaptive systems: genetic
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algorithms, genetic programming, evolutionary strategies, and related methods
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grounded in variation, inheritance, and selection. The label \emph{Evolon}
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derives from Latin \emph{evolutio} (``unrolling, unfolding'') via
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\emph{evolution}, plus \emph{-on} as a unit suffix. \emph{Evolon} names ``a
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unit of evolutionary adaptation''---that is, a system whose primary
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operation is the evolutionary updating of a population of candidate
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solutions.
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\item[\textbf{Sym} --- \emph{Symon}.]
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The mechanism \Sym~denotes symbolic reasoning: rule-based expert systems,
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theorem provers, logic programming, and other forms of explicit symbolic
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manipulation. The label \emph{Symon} is rooted in Greek \emph{symbolon}
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(``token, sign'') and \emph{symballein} (``to throw together, to compare''),
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via Latin \emph{symbolum} and modern English \emph{symbol}. The \emph{-on}
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suffix again marks a unit or agent, so \emph{Symon} denotes systems whose
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defining operation is the manipulation of explicit symbols and rules.
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\item[\textbf{Prb} --- \emph{Probion}.]
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The mechanism \Prb~captures probabilistic inference: Bayesian networks,
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probabilistic graphical models, Monte Carlo methods, and related stochastic
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reasoning tools. The label \emph{Probion} derives from Latin
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\emph{probabilis} (``provable, likely'') via \emph{probability}, plus
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\emph{-on}. A \emph{Probion} system is one whose central operative premise is
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updating or querying probability distributions, rather than deterministic
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logic, parametric learning, or search over explicit alternatives.
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\item[\textbf{Sch} --- \emph{Scholon}.]
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The mechanism \Sch~covers search and related operations: heuristic search,
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combinatorial optimization, constraint satisfaction, and state-space
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exploration. The label \emph{Scholon} is based on Greek \emph{scholē}
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(``leisure devoted to learning, study'') and its descendants in Latin
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\emph{schola} and modern English \emph{school}, \emph{scholastic}. These
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terms historically refer to structured inquiry and systematic examination.
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The \emph{-on} suffix yields \emph{Scholon} as ``an agent or unit of ordered
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inquiry'', emphasizing that \Sch mechanisms operate by disciplined search
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through a space of possibilities.
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\item[\textbf{Ctl} --- \emph{Controlon}.]
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The mechanism \Ctl~denotes control and feedback systems: classical PID
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controllers, modern state-space controllers, and feedback architectures that
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adjust actions based on error or state estimates. The label \emph{Controlon}
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derives from English \emph{control}, itself from Old French
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\emph{contrerolle} (``a register, a counter-roll'') and Medieval Latin
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\emph{contrarotulus}. In OPT usage, \emph{Controlon} refers to systems whose
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defining operation is closed-loop regulation around a target, rather than
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learning a model, performing search, or conducting probabilistic inference.
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\item[\textbf{Swm} --- \emph{Swarmon}.]
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The mechanism \Swm~comprises swarm and collective-behavior systems:
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particle-swarm optimization, ant-colony optimization, boids-like flocking,
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and other methods based on many simple agents following local rules. The
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label \emph{Swarmon} blends English \emph{swarm}, from Old English
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\emph{swarma} (``a mass of bees or other insects in motion''), with the
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\emph{-ion/-on} particle suffix. A \emph{Swarmon} system is characterized by
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emergent behavior from populations of locally interacting units, rather than
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global parametric learning or a single, centralized search procedure.
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\end{description}
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Taken together, these labels provide a mnemonic and etymologically grounded
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lexicon for referring to OPT mechanisms at a slightly more narrative level
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than the three-letter codes. They are intended as aids to memory and
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exposition; the formal taxonomy remains defined in terms of the canonical
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roots \Lrn, \Evo, \Sym, \Prb, \Sch, \Ctl, and \Swm.
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