In response to increasingly complex sets of design criteria faced within architectural practice, this investigation sets out to interrogate the prevalently passive role of the model within contemporary architectural design discourse. Through the adoption of the algorithm as a primary design tool, it posits the model as an active site of programmable collective intelligence - one that is able to inform its own development internally. This model is in essence a synthetic self-organizing system producing architectural order that emerges from an embedded ecology of simple distributed decision-making routines defined by the architect. For obvious reasons, such an approach has significant implications on the role of the architect within the design process. Complex design problems are no longer solved directly by this singular high-level decision-maker. Rather, they are approached indirectly through the programming and deployment of many low-level decision-making entities acting in parallel. This is the same decentralized approach nature takes in producing its various architectures which can only be admired for their sophistication. To the architect, it presents a new, largely uncharted, domain of design solutions - one that was previously inaccessible through traditional top-down design methodologies. In order to traverse this territory with intent, however, the architect must be fluent with the non-linearity of self-organization - where the nature of the whole is not necessarily evident in the nature of the part. Without a sense of what types of local rules give rise to what types of global order how can these generative agencies be critically programmed? Based on this line inquiry, the following aims to build the necessary intuitions of emergent phenomena by examining goal-oriented mechanisms of self-organization in natural systems. From here, construction can begin on this new autonomous model of architecture.

This scientific work focuses on computer-aided computational models in architecture. The author initially investigates established computational models and then expands these with newer approaches to modeling. In his research the author integrates approaches to analytical philosophy, probability theory, formal logic, quantum physics, abstract algebra, computer-aided design, computer graphics, glossematics, machine learning, architecture, and others. For researchers in the fields of information technology and architecture.

Researchers and data analysts are increasingly relying on graphical tools to assist them in modeling their data, generating their hypotheses, and gaining deeper insights on their experimentally acquired data. Recent advances in technology have made available more improved and novel modeling and analysis media that facilitate intuitive, task-driven exploratory analysis and manipulation of the displayed graphical representations. In order to utilize these emerging technologies researchers must be able to transform experimentally acquired data vectors into a visual form or secondary representation that has a simple structure and, is easily transferable into the media. As well, it is essential that it can be modified or manipulated within the display environment. This thesis presents a data-driven modeling technique that utilizes the basic learning strategy of an unsupervised clustering algorithm, called the self-organizing feature map, to adaptively learn topological associations inherent in the data and preserve them within the topology imposed by its predefined spherical lattice, thereby transforming the data into a 3D tessellated form. The tessellated graphical forms originate from a sphere thereby simplifying the process of computing its transformation parameters on re-orientation within an interactive, task-driven, graphical display medium. A variety of data sets including six sets of scattered 3D coordinate data, chaotic attractor data, the more commonly used Fisher s Iris flower data, medical numeric data, geographic and environmental data are used to illustrate the data-driven modeling and visualization mechanism. The modeling algorithm is first applied to scattered 3D coordinate data to understand the influence of the spherical topology on data organization. Two cases are examined, one in which the integrity of the spherical lattice is maintained during learning and, the second, in which the inter-node connections in the spherical lattice are adaptively changed during learning. In the analysis, scattered coordinate data of freeform objects with topology equivalent to a sphere and those whose topology is not equivalent to a sphere are used. Experiments demonstrate that it is possible to get reasonably good results with the degree of resemblance, determined by an average of the total normalized error measure, ranging from 6.2x10-5 1.1x10-3. The experimental analysis using scattered coordinate data facilitates an understanding of the algorithm and provides evidence of the topology-preserving capability of the spherical self-organizing feature map. The algorithm is later implemented using abstract, seemingly random, numeric data. Unlike in the case of 3D coordinate data, wherein the SOFM lattice is in the same coordinate frame (domain) as the input vectors, the numeric data is abstract. The criterion for deforming the spherical lattice is determined using mathematical and statistical functions as measures-of information that are tailored to reflect some aspect of meaningful, tangible, inter-vector relationships or associations embedded in the spatial data that reveal some physical aspect of the data. These measures are largely application-dependent and need to be defined by the data analyst or an expert. Interpretation of the resulting 3D tessellated graphical representation or form (glyph) is more complex and task dependent as compared to that of scattered coordinate data. Very simple measures are used in this analysis in order to facilitate discussion of the underlying mechanism to transform abstract numeric data into 3D graphical forms or glyphs. Several data sets are used in the analysis to illustrate how novel characteristics hidden in the data, and not easily apparent in the string of numbers, can be reflected via 3D graphical forms. The proposed data-driven modeling approach provides a viable mechanism to generate 3D tessellated representations of data that can be easily transferred to a graphical modeling and ana

Models are an essential component of the architect's design process. As tools of translation, models assist the exploration of the possible and illustrate the actual. While models have traditionally served as representational and structural studies, they are increasingly being used to suggest and solve new spatial and structural configurations. Models, the eleventh volume of the highly regarded journal 306090, explores the role of the architectural model today in relation to the idea, the diagram, the technique, and the material. Models includes contributions from engineers, scientists, poets, painters, photographers, historians, urbanists, and architects both young and experienced.

This book constitutes the refereed proceedings of the Second International Workshop on Self-Organizing Systems, IWSOS 2007. The 17 revised full papers and five revised short papers presented together with two invited talks were carefully selected from more than 36 submissions. The papers are organized in topical sections on ad hoc routing, peer-to-peer networking, network topology, adaptive and self-organizing networks and multicast and mobility protocols.

Mastering the sensorimotor capabilities of our body is a skill that we acquire and refine over time, starting at the prenatal stages of development. This learning process is linked to brain development and is shaped by the rich set of multimodal information experienced while exploring and interacting with the environment. Evidence coming from neuroscience suggests the brain forms and mantains body representations as the main strategy to this mastering. Although it is still not clear how this knowledge is represented in our brain, it is reasonable to think that such internal models of the body undergo a continuous process of adaptation. They need to match growing corporal dimensions during development, as well as temporary changes in the characteristics of the body, such as the transient morphological alterations produced by the usage of tools. In the robotics community there is an increasing interest in reproducing similar mechanisms in artificial agents, mainly motivated by the aim of producing autonomous adaptive systems that can deal with complexity and uncertainty in human environments. Although promising results have been achieved in the context of sensorimotor learning and autonomous generation of body representations, it is still not clear how such low-level representations can be scaled up to more complex motor skills and how they can enable the development of cognitive capabilities. Recent findings from behavioural and brain studies suggests that processes of mental simulations of action-perception loops are likely to be executed in our brain and are dependent on internal motor representations. The capability to simulate sensorimotor experience might represent a key mechanism behind the implementation of further cognitive skills, such as self-detection, self-other distinction and imitation. Empirical investigation on the functioning of similar processes in the brain and on their implementation in artificial agents is fragmented. This e-book comprises a collection of manuscripts published by Frontiers in Robotics and Artificial Intelligence, under the section Humanoid Robotics, on the research topic re-enactment of sensorimotor experience for cognition in artificial agents. This compendium aims at condensing the latest theoretical, review and experimental studies that address new paradigms for learning and integrating multimodal sensorimotor information in artificial agents, re-use of the sensorimotor experience for cognitive development and further construction of more complex strategies and behaviours using these concepts. The authors would like to thank M.A. Dylan Andrade for his art work for the cover.