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dc.contributor.advisorCampagnol, Gabriela
dc.contributor.advisorShepley, Mardelle M
dc.creatorMullican, Raymond Charles
dc.date.accessioned2019-01-16T16:59:28Z
dc.date.created2017-12
dc.date.issued2018-01-19
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1969.1/173032
dc.description.abstractWith the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectadaptive configurationen
dc.subjectadaptive fielden
dc.subjectarchitectural configuration spaceen
dc.subjectarchitectural descriptoren
dc.subjectarchitectural graphen
dc.subjectarchitectural identityen
dc.subjectarchitectural seten
dc.subjectarchitectural trajectoryen
dc.subjectbinding configurationen
dc.subjectbipartite recursionen
dc.subjectBoolean neighborhood groupen
dc.subjectBoolean topologyen
dc.subjectcausative configurationen
dc.subjectcertainty-path graphen
dc.subjectcompletely connected topological spaceen
dc.subjectcompletely developed spaceen
dc.subjectcomplex systemen
dc.subjectconfigurationen
dc.subjectconfiguration spaceen
dc.subjectconnected neighborhooden
dc.subjectcritical stateen
dc.subjectdeductive inference graphen
dc.subjectdeductive inference spaceen
dc.subjectevolutionary trajectoryen
dc.subjectextended genotypeen
dc.subjectextended phenotypeen
dc.subjectgiant componenten
dc.subjecthuman niche constructionen
dc.subjectin absentia modelingen
dc.subjectinductive inference spaceen
dc.subjectinternal order parameteren
dc.subjectlimit-point neighborhooden
dc.subjectlocal phase transitionen
dc.subjectlocal probability spaceen
dc.subjectlocal sample spaceen
dc.subjectlocally complete metric spaceen
dc.subjectlocally complete probability spaceen
dc.subjectmemeen
dc.subjectmemeticen
dc.subjectmemotypeen
dc.subjectnon-bindingen
dc.subjectnonlinearen
dc.subjectparallel causationen
dc.subjectparallel processingen
dc.subjectpercolation thresholden
dc.subjectPoincare inflationen
dc.subjectprobabilistic fitness landscapeen
dc.subjectprobability distanceen
dc.subjectprobability-metric indexen
dc.subjectprocess-structure–property–performanceen
dc.subjectprocess-structure modelen
dc.subjectquasi-binding configurationen
dc.subjectself-assemblyen
dc.subjectself-organizationen
dc.subjectself-organized systemen
dc.subjectsemi-metric probability spaceen
dc.subjectsemi-metric topological spaceen
dc.subjectsituated conditional probability chainen
dc.subjectsituated connectivityen
dc.subjectsituated mathematicsen
dc.subjectsituated modelingen
dc.subjectsituated probabilityen
dc.subjectsituated structural modelingen
dc.subjectstructural coherenceen
dc.subjectstructural phase transitionen
dc.subjectstructure-property modelen
dc.subjectsubjective fitnessen
dc.subjecttopological distanceen
dc.subjecttopological-probability spaceen
dc.subjecttopologic-metric indexen
dc.titleInductive Pattern Formationen
dc.typeThesisen
thesis.degree.departmentArchitectureen
thesis.degree.disciplineArchitectureen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberWalton, Jay R
dc.contributor.committeeMemberPopp, Robert K
dc.type.materialtexten
dc.date.updated2019-01-16T16:59:28Z
local.embargo.terms2019-12-01
local.embargo.lift2019-12-01
local.etdauthor.orcid0000-0002-0652-9496


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