Semester2 llk final

EMERGENT SYSTEMS

LLK University of Melbourne MSD 2013

KIM CHAN

626067

LUKE MADDEN

359911

LAURA NG

358582

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Abstract

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ABSTRACT

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Emergent systems are one of the many new exciting approaches being introduced into the architectural field of design. Emergent systems provide a designer with the ability to simulate and understand behaviour and interactions, creating the potential for considered and evolutionary multi-disciplinary solutions using informed logic of the users they are designing for.

LLK have explored the concept of emergence in architecture and its provision of simple solutions to complex issues experienced by the changing needs of a dynamic and evolving society.

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Abstract

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Acknowledgement

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ACKNOWLEDGEMENT

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To Chiara Colli, Salvatore Marino, Uberto Pignatti Morano and Filippo Nassetti, the creators of pa nD[imensional].A[rchitecture]. Their project has provided invaluable insight into the technical side of creating systems.

To Wayne Madden, who provided technical guidance throughout the project, without which it may not have been accomplished.

To Linus Tan, tutor of Studio In{TR}Ex: Emergent Systems, who provided critical feedback and guidance in what started as an unfamiliar field.

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Acknowledgement

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Content

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CONTENT

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Generative systems, agent-based systems and emergent systems were all explored within the scope of this studio. Using the research from literature reviews and case studies, concepts of emergence were applied to three projects: the design of a system to generate a lamp for project 1 and a pattern for project 2, informing the final project, project 3, the design of a system for an architectural design situated in Melbourne’s CBD.

CH00 | Preface

Content

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CH01 | Introduction

Research Topic

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RESEARCH TOPIC

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The use of digital tools has been a turning point in the architectural world, with new possibilities created and existing ones finally being given the potential to be realised. One such area to benefit from such technological advance is the design of emergent systems. Utilising the power of computational technologies, designers now have a reliable and accessible method for designing and controlling simulations of systems. This possibility will be explored by LLK, culminating in a final design project informed by emergent systems.

CH01 | Introduction

Research Topic

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CH01 | Introduction

Research Question

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RESEARCH QUESTION

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LLK intend to explore the potential use of stigmergy as a functional and flexible system for laying down the framework for architectural design, taking into account various site conditions and humanistic characteristics.

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Research Question

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CONTENTS + STRUCTURE

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CH01 | Introduction

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PREFACE

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Abstract

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Acknowledgements

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Content

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INTRODUCTION

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Research Topic

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Research Question

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Contents + Structure

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Contents + Structure

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CH01 | Introduction

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SYSTEM INVESTIGATION

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Generative Systems

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Literature Review

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Case Studies

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Agent-Based Systems

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Literature Review

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Case Studies

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Emergent Systems

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Literature Review

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METHODOLOGY + EXPERIMENTATION

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Generative Systems

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Intent + Conditions

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Methodology

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Method Description

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Evaluation Criteria

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Experimentation

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Outcomes

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Conclusion

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Contents + Structure

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Agent-Based Systems

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Intent + Conditions

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Methodology

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Method Description

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Evaluation Criteria

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Experimentation

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Outcomes

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Conclusion

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Emergent Systems

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Intent + Conditions

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Methodology

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Method Description

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Evaluation Criteria

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Experimentation

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Outcomes

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Conclusion

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CH01 | Introduction

Contents + Structure

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CH01 | Introduction

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ARCHITECTURAL DESIGN

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Design Intent | Design Brief

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Site Analysis

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Method Application

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Outcomes

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Conclusion

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REFERENCES

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ILLUSTRATIONS

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Contents + Structure

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Chapter 02

SYSTEMS INVESTIGATION

CH03 | Experimentation

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GENERATIVE SYSTEMS

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Generative Generative systems are systems consisting of internal forces which are influenced by growth.

They use a limited number of variables to create an outcome, often producing a wide variety of results which are not visibly similar. Generative systems use one basic formula made up of these variables but are capable of producing a seemingly infinite number of results. As such, generative systems are often suitable for tasks which require a wide variety of results to assess, and are capable of rapid prototyping.

CH02 | Investigation

Generative Systems

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LITERATURE REVIEW

Self-organisation and Material Constructions 034 - 041 Michael Weinstock, 2006 Digital Morphogenesis 032 - 037 Neil Leach, 2009

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A literative review of writings by both Leach and Weinstock established an initial understanding of generative systems, presented concepts and analysed the potential applications of these systems within the field of science, and wihin the built environment, focusing on urban design and architectural examples. A number of key concepts were introduced and elaborated on in the readings, where those bearing the most crucial information in relation to comprehending generative systems are described:

| Biomimetics Studying and replicating the mechanisms of nature can provide interesting and useful results, due to the inherently optimal results they produce. Many generative systems are inspired by nature, and derive their function from demonstrated examples of their application. | Morphogenesis Through the processes of growth and differentiation, it is possible to create forms using bottom-up logic. This concept strongly emphasises performance over appearance, and process over representation. | Performance The highly efficient possibilities of generative systems corresponds to the possibility of highperformance solutions. This exists in both form and structural optimisation, often which are integrated into a single process.

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Literature Review

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| Processes Generative systems place the emphasis on process rather than final form in order to produce the most optimal solution for a design. As such, the designer becomes a controller of these processes, looking at their dynamic interactions and components. | Efficiency Generative systems are used to solve design issues in a highly efficient way, due to the carefully cultivated conditions which they perform under.

The idea of efficiency in generative systems is closely linked to that of biomimetics, often being the initial reason a biological system is studied.

| Form Finding Generative systems shift the focus from aesthetic appeal to a meaningful process of design. The resulting form of generative processes is based on a set of initial parameters, so the designer is not directly influencing or controlling the form. | Behaviour Given that a generative system “forms itself�, it is often beneficial to analyse the behaviour that leads to the resultant form. By doing this, it is possible to recreate such forms when performed under specific stimuli, leading to applications in structure, form and beyond.

| Logic This is the guiding factor of a generative system.

It enables the system to determine what action is taken in response to any particular force or action by following the sequence of functions set up by the systems designer in a predictable manner.

CH02 | Investigation

Literature Review

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CASE STUDIES Proto Towers

Christoph Hermann, 2010

LLK 019 Figure 2.0 Proto Towers Figure 2.1 Proto Towers

Hermann’s ‘Proto Towers’ demonstrates a number of the key concepts discussed in the reviewed literature. Despite perceptions of irregularity, the resulting form displays self-organising qualities which have been ordered into the most efficient arrangement.

Regardless of the upcoming tasks being led by a bottom-up design approach, such studies are beneficial in assisting to visualise how certain behaviours can affect the outcome of a system.

CH02 | Investigation

Case Studies

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AGENT-BASED SYSTEMS

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Agent-Based Agent-based systems are composed of autonomous agents whose collective behaviour form complex interactions which are otherwise unpredictable when examining individual agents.

As such, agent-based systems are often used as a way to assess the conditions required for equilibrium to be achieved in a particular system, therefore making them an excellent model for simulating basic behaviours of organisms as seen in nature, such as circulation and movement in groups.

CH02 | Investigation

Agent-Based Systems

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LITERATURE REVIEW

Swarm Modelling - The use of Swarm Intelligence to generate architectural form 001 - 021 Miranda Carranza + Paul Coates, 2000 Swarm Urbanism 056 - 063 LLK

Neil Leach, 2009

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Once again, the readings were used to establish an initial understanding of the systems to be recreated.

The key concepts extracted from these readings on agent-based systems are discussed: | Intelligence Individual agents in an agent-based systems are encoded with a degree of intelligence as a requirement to function in the system. It is this intelligence that allows agents to act autonomously by assessing each situation separately, despite their interactions with other agents. These actions do affect the overall behaviour of the collection of agents (commonly referred to as flocks or swarms), but is analysed separately as the actions of a swarm do not necessarily reflect the actions of individual agents, or vice-versa.

CH02 | Investigation

| Swarm Intelligence Even though the agents are capable of individual behaviour which responds to processes and stimuli at a local level, these interactions produce effects that go beyond the boundaries of their own cognition. With This level of intelligence is able to manifest itself despite the fact that it may not specifically be encoded into the individual agent. Collectively, the results of these individual actions can produce complex behaviour which is not able to be observed in a single agent, nor is it able to be predicted based on the behaviour of a single agent.

Literature Review

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| Interactions In order to produce the complex behaviour observed by flocks of agents which demonstrate swarm intelligence, there must be a level of interaction between the agents.

Without this constantly changing level of influence, the actions of the agents would be completely predictable, and for the most part, have very little use in experimentation.

It is the interractions between agents which facilitate the complex phenomena that are so highly valued in testing environments in design discplines and beyond.

This ability to self-organise without any topdown intervention by the system designer is one of the most crucial traits of an agent-based system, as it is this result which the system designer is investigating to find the process which has ultimately produced.

| Self-Organisation As the behaviour of the agents will take the surrounding environment into account when responding to a specific stimulus, the result is an organised swarm. The way in which it is arranged is dependent on the intelligence of the agents and swarm intelligence of the flock, and as such, it may not be visibly organised.

CH02 | Investigation

Literature Review

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CASE STUDIES Topological Silk Stretching

Qi Su + Shenyuan Guo, 2009

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Topological Silk Stretching exists as a theoretical self-generated structure, made to be both porous and adaptive. Studying this project allows the viewer to familiarise themself with some of the basic concepts of agent based systems (swarm intelligence, interaction and self-organisation to name a few) in terms of how they are represented visually.

Figure 2.2 Topological Silk Stretching

Such familiarity is crucial for the upcoming design task, given that the ultimate outcome of the task involves a visual component which is directly related to the function of the project.

CH02 | Investigation

Case Studies

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EMERGENT SYSTEMS

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Emergent Emergent systems are systems which emerge as a result of complex interactions between a multiplicity of relatively simple individual components. Emergent systems commonly occur in nature, such as the formation of complex geometry in snow flakes and termite colonies and thus make excellent applications for the simulation of real world phenomena. The interactions between agents in emergent systems can be seen as a way of learning from each other, and thus creating a system which evolves based on the interactions of the agents within.

CH02 | Investigation

Emergent Systems

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LITERATURE REVIEW Emergence in Architecture 006 - 009 Michael Hensel + Achim Menges, 2004 The Generative Multi-Performance Design System 448 - 457 Anas Alfarais + Riccardo Merello, 2008

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These readings combine the concepts explored by the literature of generative and agent-based systems. These emergent systems readings served more as a tool for understanding the evaluation of such systems, rather than a simple introduction.

The key ideas and process of evaluating these systems is discussed below. | Holistic Design problems are often open ended, leading to the probability of multiple potential solutions.

The open nature of design problems also opens up the possibility of multi-disciplinary issues which require a set of interacting systems to produce a legitimate solution. This non-linear reasoning demands multiple considerations at any given point. These interacting systems, like agents, are capable of producing complex behaviour which is not observable or predictable from an individual system, and thus must be analysed together as a whole through a holistic approach.

CH02 | Investigation

| Iterations Evaluation of a system is used to identify its strengths and weaknesses in addition to assessing how well it is meeting the criteria for which it was designed. A continuous process of modification and further evaluation allows a system to become more effective, essentially meaning that it solves the problem to a fuller extent. Each iteration is an improvement of the last, with modifications being made based on the results of the most recent evaluation results.

Literature Review

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Additionaly, there are four key stages to the Generative Multi-Performance Design System (GMPDS) discussed by Alfaris and Merello: | Synthesis This stage involves the generation of the initial prototype based on extracting design intentions and using them to create a set of parameters to guide it. | Analysis After the prototype has been created, it must be analysed to see how well it meets the goals it has been initially set with. As a design will often face multi-disciplinary problems, so the analysis must respond accordingly.

CH02 | Investigation

| Evaluation Similar to analysis, the evaluation stage is conducted to assess the quality of the solution. In addition, the evaluation may be more successful in some areas than others, which will often lead to a number of potential future modifications. | Optimisation Once the solution has been properly studied, the designer can then make changes to optimise the function of the system. These changes will be carried out as informed alterations based on the previous analysis and evaluation.

Literature Review

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Chapter 03

METHODOLOGY + EXPERIMENTATION

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GENERATIVE SYSTEMS

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Generative systems use algorithms to design and create an outcome. In this project, tessellations were utilised as the material system. This system of tessellations provides a cover, or a surface, through the formation of repeated single geometry, without any gaps (Tan, 2013).

From the concepts explored by Hermann, Leach and Weinstock, the following aspects were used to specifically inform the generative design process: | Biomimcry | Logic | Form-finding An abstraction of the principles from existing biological processes in nature informed a material system that displays self-organising qualities based on an existing set of functioning rules.

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into one single process provided the basis for the overall design. In conjunction with biomimicry, the system also uses logic to determine an action in reponse to an internal force or experience that is influencing the system. Nature similarly operates according to a logic of optimisation, allowing for systems to perform at an optimal level within the set parameters, in this scenario, involving the transmittance of adequate light as a table lamp.

Additionally, the form emerges as a direct result of bottom-up functions and processes based on the set parameters.

These concepts were used to inform the generative design process that involves the optimisation of the lamp so that it integrates form, material and structure all as one entity based on a set of functioning rules, opposed to material influences of aesthetics, evolving throughout a bottom-up process. The advantages of this process is that it generates an infinite field of variation from which to determine the optimal outcome.

Generative Systems

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INTENT + CONDITIONS

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Task For this project, the task was to design a rulebased system that generates a table lamp. By implementing a generative system, an infinite number of solutions are generated, neccessitating the processes of evaluation, elimination and optimal selection, this being the purpose of the experimentations.

Conditions The outcome needs to satisfy the following three rules: | Generation The design must be influenced by a light source | Degeneration The lamp must accomodate the light bulb and permit light | Equilibrium Physical constraints must be limited within 180x180x180mm

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Intent + Conditions

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Intent Being considerate of both the task and the rules, the intent was to generate a system that designs a table lamp which emphasises the direct influence of light and the resultant shadow, and consequent pattern formations, as well as the relationship between surface openings. In this sense, the purpose of the lamp is as a decorative element of accent lighting through the generation of visual interest.

Figure 3.0 Controlled Components: of light intensity, layering and opening size Figure 3.1 Light and shadows Figure 3.2 Relationship of openings

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Intent + Conditions

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METHODOLOGY

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The design intent is driven by the integration of the following concepts of: | Light and Shadows | Relationship between surface openings In order to successfully emphasise this design intent, a number of components were required to be controlled:

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| Light Intensity When a surface is closer to a light source it casts a comparatively bigger shadow than when further from a light source, where a smaller shadow results. Furthermore, light from a higher source relative to the surface produces a short shadow, while a lower source creates a longer shadow. Hence, the distance and angle between light source and surface can result in varying effects. This is exemplifed through the Nervous System’s ‘Hyphae Lamp’ (Figure 3.4) which is generated algorithmically based on processes of leaf vein formation, where each result casts a unique pattern of shadows based on the generated form and the amount of light that is able to pass through, having diverse effects on the surrounding atmosphere (Rosenkrantz, 2013).

Methodology

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| Layering Layering creates a relationship between the openings, as well as an opportunity to cast shadows both internally and externally to the lamp’s surface. Thereby allowing for a layering effect of shadows to intensify visual interest.

Figure 3.3 Nervous System’s Hyphae Lamp

| Opening Size Opening sizes are capable of controlling the depth, direction and angle of the resulting shadows. The openings themselves either permit light or block light to cast specific shadows.

Other considerations in relation to the predetermined rules included the direct influence of light and the resultant shadow and examination of how the bulb may be used to limit the luminosity of the lamp. The implementation plan for designing the generative system also considered the final method of fabrication, where 3D printing would require a minimum structural depth of three millimeters (3mm).

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Methodology

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Inputs A series of inputs were implemented and determined by selecting the properties of light and shadow and the opening relationship that was intended to be showcased, such as the intensity of light, defined shadows and overlapping shadows.

Coded information, material system inputs and design influences were all considered. The following coded information was required:

In terms of design influence, the inputs consisted of fabrication consideration, such as the thickness of each initial surface. Control of the intensity of the light source by modifying the intial geometry pattern also was considered: | Fabrication Consideration | Control of the Light Intensity by Initial (Pattern) Geometry Modification

|Location of the Light Bulb |Initial Surface Geometry |Initial (Pattern) Geometry The material system itself needed to be taken into consideration of the inputs and intent. As tessellations are a repeated geometry fully filling a surface without any gaps, void design was integrated to create interest and break up the repetition of the geometry, and at the same time, providing gaps within the inital pattern geometry.

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Methodology

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METHOD DESCRIPTION

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Implementation of the inputs involved a series of functions in Grasshopper. These included: | Layering Surface Geometry | Variation in Size Openings | Attractors and Scaling

For each experimentation the inputs are varied, in accordance with these controlling functions in Grasshopper. These include: |Void Quantity Determined by the number of panels created in the u and v directions. |Surface Geometry Controlled by functions of sine (sin) and cosine (cos). |Void Size Relative to the Initial Geometry Controlled by independent scaling of left, right, internal and external voids. |Void Size Relative to the Attractor Points of the Surface Geometry Determined by attractor graph coordinates.

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Method Description

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EVALUATION CRITERIA

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Inputs To determine whether the experimentations were successful or required additonal exploration, a set of evaluative criteria were predetermined based on the intent and the inputs. These evaluative criteria were analysed according to visual judgement: |Constructibility Can it support itself sufficiently? What percentage is a void space that defines the opening and what percentage generates the structure? Are there adequate joints for production without failure, for 3D printing? |Light and Shadow Intensity Can the light pass through the surface? Does the shadow or the light dominate? If the shadow dominates, it should do so without completely sacrificing the light.

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|Layering Is there too much layering concentrated in one area of the form? Is there too much layering that detracts from the shadow patterns being cast? Is there insufficient layering that the bulb is visible? |Vision of the Bulb Should the light source be visible? A visible bulb detracts from the overall shape as well as the shadow effects generated. |Shadow Effect Does the shadow create a pattern of interest?

Do the opening sizes allow sufficient light to pass through? If the void is too small, it casts only shadow, what is the purpose of a void that permits no light? If it is too big, it permits only light.

Evaluation Criteria

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By using this set of criteria, the final outcome would remain consistent with the intent of shadow and light and express a relationship between the openings.

CH03 | Experimentation

Evaluation Criteria

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EXPERIMENTATION

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Generative Systems Coding Tessellating a surface to create any geometry with variations in opening location and opening size, both internally and externally, was generated through the digital tool of Grasshopper. The final phase of the process involved the visualisation of the the geometry to test its shadow performance.

Figure 3.4 Base geometry Grasshopper code implemented for the generative design

The grasshopper script for this tessellation used a base geometry. The base geometry was created by a mathematical expression which can be manipulated to achieve other geometries.

Additionally, the base geometry was also included in the shadow performance testing process.

CH03 | Experimentation

Experimentation

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LLK 038 Figure 3.5 Results of the base geometry

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Experimentation

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The base geometry was then subdivided into even rectangular segments. The amount of segments determines the degree of smoothness through panellisation. When increased, it results in a smooth geometry. Essentially this forms the basis from which the openings could then be generated on.

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Figure 3.6 Division of the base geometry

Experimentation

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LLK 040 Figure 3.7 Results of the division of the base geometry

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Experimentation

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Segments were exploded in order to create two lists of triangular surfaces by selecting each corner point of the exploded face.

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Figure 3.8 Division of the panellisations into two sets of triangles

Experimentation

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LLK 042 Figure 3.9 Results of the division of the surface into two sets of traingles

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Experimentation

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The next step was to create an opening from the list of triangular surfaces to satisfy the requirements for light emittance. This was achieved by evaluating the central point of the triangles and creating a smaller triangle within, forming the voids. Void sizes are controlled by attractor points and by the distance between each central point, where the closer the attractor point, the smaller the opening.

Figure 3.10 Openings created in relation to attractor points

Two lists of triangular surfaces with openings were joint together to create the original geometry.

Each of the design inputs can be varied endlessly, however the variations must be analysed using the evaluative criteria in order to generate the optimal result.

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Experimentation

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LLK 044 Figure 3.11 Results of the generation of openings on the surface using attractor points

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Experimentation

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The following iterations are variations of the code previously discussed. Each iteration is broken up into a series of stages where the evaluation of one iteration became the input for the next: Iteration 1.0 | Layering of Surface Geometry In this iteration, the focus is on the layering of surface geometry, by altering the inputs that determine the surface geometry and the those that have an effect on panel direction and the void quantity:

The set evaluation criteria is used to analyse each stage of the experimentation: |Evaluative Criteria Constructibility Light + Shadow Intensity Layering Vision of the Light Source Shadow Effect

| Surface Geometry Determined by functions of sin(x) and cos(x) | Panel Directions + Void Quantity Determined by ‘u’ and ‘v’ values

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Experimentation

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Panel Directions + Void Quantity LLK

Surface Geometry

u 25 v 10

cos(x) sin(x)

cos(x2) sin(x2)

u 30 v 30

u 10 v 10

u 10 v 25

u 50 v 50

u 50 v 10

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046 Figure 3.12 Iteration 1.0 showing the effects of varying the inputs associated with surface geometry and panel directions + void quantity

cos(x) sin(x2)

xcos(x) xsin(x)

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Experimentation

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LLK u 25 v 10

047 Figure 3.13 The iterations selected from Iteration 1.0 as the basis for the changes to the next set iterations.

Selected in accordance with the evaluative criteria

cos(x) sin(x)

u 10 v 10

Iteration 1.1 |Variation in Size Openings: Attractors Using the optimal layering of surface geometry outcome from Iteration 1.0, inputs such as bevel direction wer experimented with to further evaluate the overall design outcome for the lamp. | Bevel Direction Determined by external and internal attractors | Panel Direction + Void Quantity Determined by functions of sin(x) and cos(x)

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Experimentation

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Bevel Direction

Panel Directions + Void Quantity

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EXT INT

u 10 v 10

u 25 v 10

(0.0, 0.0) (0.0, 0.0)

(1.0, 1.0) (1.0, 1.0)

(0.0, 0.0) (1.0, 1.0)

(1.0, 1.0) (0.0, 0.0)

(0.0, 1.0) (1.0, 0.0)

048 Figure 3.14 Iteration 1.1 showing the effects of varying the inputs associated with panel direction + void quantity and bevel directions

Experimentation

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LLK (1.0, 1.0) (0.0, 0.0)

049 Figure 3.15 The iteration selected from Iteration 1.1 as the basis for the next set iterations.

Selected in accordance with the evaluative criteria

u 25 v 10

Iteration 1.2 | Variation in Size Openings: Scaling The selected outcome from Iteration 1.1 is then subject to variations to adjacent void scaling using the optimal bevel direction. | Adjacent Void Scaling Determined by scaling of ‘left’ and ‘right’ of voids | Bevel Direction Determined by external and internal attractors

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Experimentation

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Adjacent Void Scaling LLK

Void Size

EXT INT

(0.3, 0.3) (0.0, 0.0) (0.3, (0.0, 0.3) 0.0)

(0.3, 0.9) (0.3, 0.9)

(0.9, 0.3) (0.9, 0.3)

050 Figure 3.16

(1.0, 1.0) (0.0, 0.0)

A

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(0.9, 0.9) (0.9, 0.9)

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Iteration 1.2 showing the effects of varying the inputs associated with void size and adjacent void scaling

Experimentation

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A

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B

Figure 3.17 A: Daytime view of Outcome A B: Daytime view of Outcome B

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Outcomes

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C

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052 Figure 3.18 C: Daytime view of Outcome C D: Daytime view of Outcome D

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Outcomes

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B

Figure 3.19 A: Night time view and shadow effect of Outcome A B: Night time view and shadow effect of Outcome B

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Outcomes

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C

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054 Figure 3.20 C: Night time view and shadow effect of Outcome C D: Night time view and shadow effect of Outcome D

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Outcomes

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OUTCOMES |Evaluative Criteria Constructibility Light + Shadow Intensity Layering Vision of the Light Source Shadow Effect

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055 Figure 3.21 Night time view of Outcome A

To evaluate the final four outcomes from the final generation of iterations, Outcome A, B, C and D were subject to a lighting examination to select the final outcome, based on the evaluative criteria.

Outcome A In Outcome A, the form that results has an even adjacent void scaling. As the openings are consistent with one another across the entire form, it provides a structure with a thickness suitable for construction. However, due to the dominance of the solid matter over the openings, shadows are more prominent than the light. As well, the layering with minimal openings results in areas where there is little or no light transmittance from the bulb to the surroundings. However, this positively impacts on the intended bulb visibility, where the bulb is sufficiently hidden within the structure and the shadows. The regularity of the openings is reflected through the overall shadow effect providing an effect that is aesthetically pleasing to the eye.

CH03 | Experimentation

Outcomes

03

01

06

|Evaluative Criteria Constructibility Light + Shadow Intensity Layering Vision of the Light Source Shadow Effect

LLK

B

056

Outcome B For this outcome, on one side of the panels, all of the opening voids are considerably larger. Due to the enlarged openings in comparison to Outcome B, the constructibility of the form is less convincing.

Despite this, there is a greater strength of light intensity, ultimately allowing for the form to perform successfully as a lamp. Even at the point of greatest layering, there is still sufficient light penetration to the surroundings. Despite the increase in size of the voids, the light source is still not visible, thereby directing the vision of the viewer to the form and the consequent shadow effect. The shadow effect expresses a distinct contrast between light and shadow where both are evenly distributed. However due to the angularity of the form, and the amount of light being emitted and reflected within the lamp, the direction is drawn towards the lamp, rather than the intended effects of the lamp - the shadow effect.

CH03 | Experimentation

Figure 3.22 Night time view of Outcome B

Outcomes

03

01

06

|Evaluative Criteria Constructibility Light + Shadow Intensity Layering Vision of the Light Source Shadow Effect

LLK

C

057 Figure 3.23 Night time view of Outcome C

CH03 | Experimentation

Outcome C Outcome C provides the form with optimal structural stability due to panels that have both a small and large void on either side. This creates a dense form where there is a significant contrast between the shadow and light intensity. In this case the shadow is dominant, however there is still an efficient amount of light to provide a strong shadow effect. The layering of the form in conjunction with the smaller openings means that in areas of highest layering, the light is unable to pass through from the bulb to the exterior, however, this does create shadows that are both within the folds of the layering as well as externally to the surroundings. Thereby creating a strong relationship between the layering and the shadow effect. The increased density of the form and layering sufficiently hides the bulb to allow the viewer to observe the smooth, organic form that is generated. The overall shadow effect creates a pattern of interest where shadows are both internal and external and a soft light is subtly distributed to the surroundings.

Outcomes

03

01

06

|Evaluative Criteria Constructibility Light + Shadow Intensity Layering Vision of the Light Source Shadow Effect

LLK

D

058

Outcome D Outcome D shows the generation of a form with qualities that are considerably different to that of Outcome C. The form that has generated appears quite fragile due to the very refined joints as a result of the void sizing being increased to near their maximum. The light intensity is very strong, with shadow being cast only externally to the surface. Due to the large openings along one side of the panelling, a visual connection to the whole remains no matter the angle of the viewer. The layers are clearly visible, however, as is the source of light. Despite having an optimal amount of light being emitted from the lamp, the contructibility is questionable, along with the fact that the shadow cast consists mainly of two full traingulations of shadow or light. From some perspectives, the voids within the openings are restricted by their size and are unable to permit light through the cast a shadow effect of great interest.

CH03 | Experimentation

Figure 3.24 Night time view of Outcome D

Outcomes

03

01

07

CONCLUSION

LLK 059

Outcome C was selected as the optimal lamp design based on the evaluative criteria. Most importantly it creates a shadow effect of interest while still emitting light to be used effectively as a decorative table lamp.

Figure 3.25 Daytime view of the final lamp

Generative systems employ a continual process of modification with endless possible outcomes.

A cycle of modification and evaluation creates a system the solves conflicting inputs and issues more effectively. This process used to generate the lamp used real-time data allowing for progression and evaluation through iterative processes at each stage of the design process.

CH03 | Experimentation

Conclusion

03

01

07

C

LLK 060 Figure 3.26 Night time view of the final outcome

CH03 | Experimentation

Conclusion

03

02

00

AGENT-BASED SYSTEMS

LLK 061

Agent-based systems involve systems that simulate the actions and interactions of a series of autonomous agents to achieve self-organisation without any input beyond their environment. In this project, stigmergy was utilised as the material behaviour that informed the design intent. As a behaviour, stigmergy allows communication between agents by modifying their environment through the act of leaving trails of varying strength through pheronomes.

Despite the central focus of the readings revolving predominantly around swarm intelligence, the concepts of agent-based systems explored by Carranza and Leach can be summarised by the following key words, as discussed in the Chapter 02, to inform the agent-based design process:

Figure 3.27 Kokkugia, Scheme for Melbourne Docklands: A series fo diagrams showing the effects of swam intelligence on the implementation of swarm logic to inform urban planning decisions.

| Intelligence | Interaction | Self-Organisation

CH03 | Experimentation

Agent-Based Systems

03

02

01

INTENT + CONDITIONS

LLK 062

Task For this project, the task was to design an agent-based system which self-organises into a pattern. An iterative process was used to produce different results, with the key stages of evaluation, elimination and optimisation being used to ensure the most favourable outcome is achieved.

Conditions The outcome needs to satisfy the following four rules: | Adaption The pattern must be flexible and manipulated via an agent

| Interconnectivity Multiple agent types must interact and influence each | Limitation Digital constraints must be limited within the dimensions of 2000x2000x2000mm Intent Based on the task and conditions of the project, the aim was to create a system which uses stigmergy as the main behaviour to be reproduced. In the system, the agents were to move between a set of pre-determined nodes in order to ensure the paths could be created with a purpose.

| Various constituents The system must have at least three types of agents

CH03 | Experimentation

Intent + Conditions

03

02

02

METHODOLOGY

LLK 063

In order to design the system, the agents required a main behaviour that could be encoded into the each of the agents, the behaviours are as follows: | Detection and Seeking of Nodes The agents needed to have an overall goal to which they could work to, and by introducing nodes, this would achieve that goal while also giving a meaningful reason for the agents to create and follow trails.

Figure 3.28 Early prototype of the simulation where agents successfully detect the nodes

| Leaving of Trails In order to correctly display the behaviour of a stigmergy system, the agents needed to leave trails which could be detected by other agents. | Comprehension of Trails In addition to reading the trails, agents needed to be able to evaluate their position in regards to these trails and calculate the action required to adhere to it.

CH03 | Experimentation

Methodology

03

02

02

LLK 064

Additionally, a number of variables existed which could be altered to influence the behaviour of the agents without altering the underlying logic: | Agent Speed The speed of the agents affects the overall speed of the simulation and needs to respond to the scale of the simulation environment.

| Trail Length This affected how long an agent had to react to a trail before it became undetectable, providing the possibility of distinguishing between weak and strong trails.

| Detection Distance The distance from which the agents could detect trails and nodes affects their overall goal and as such affects the way paths are formed. | Detection Range The agents are only able to detect paths in a cone in front of them with a predetermined radius in order to ensure their trails are smooth and realistic.

CH03 | Experimentation

Methodology

03

02

03

METHOD DESCRIPTION

LLK 065

The inputs and the variables defining their underlying logic were then implemented into Processing to produce three types of agent behaviours:

| Type 1 Follow any path | Follows: Type 1, 2 or 3 | Type 2 Follow any path type except their own type | Follows: Type 1 or 3 | Type 3 Follow only own path type | Follows: Type 3

CH03 | Experimentation

Method Description

03

02

03

AGENT

FIND NODE

LLK 066

IS THERE AN EXISTING PATH?

YES

FOLLOW EXISTING PATH TO NODE

NO

LEAVE PATH TO NODE

NO MAKE A PATH

DOES IT INTERSECT ANOTHER PATH? YES FOLLOW ANY PATH TYPE Type 1

CH03 | Experimentation

YES FOLLOW ANY PATH TYPE EXCEPT OWN TYPE Type 2

Figure 3.29 Demonstrates the thought process to be carried out by each autonomous agent. The process is entirely linear for individual agents, but distinctions are made for the behaviour of separate agent types

YES FOLLOW ONLY OWN PATH TYPE Type 3

Method Description

03

02

04

EVALUATION CRITERIA

LLK 067

The most basic factors by which the simulation was evaluated was by simply checking to see if the required behavioural components were demonstrated in any way. These were: | Agents actively seek out nodes | Agents are capable of leaving trails | Agents are capable of comprehending trails As discussed in the previous section, there were also a number of variables through which the simulation could be altered. These variables were the main component to be evaluated, these include: | Agent Speed Evaluated to ensure the agents are not moving through the detection area too fast to react.

CH03 | Experimentation

| Detection Distance Evaluated to ensure the distance at which the agents respond to stimuli is not so large that they detect more than they can properly respond to, and enough that they are not wandering aimlessly. | Detection Range Evaluated to ensure the agents have a realistic peripheral vision that produces reliable and consistent behaviour. | Trail Length Evaluated to ensure that the paths being created did not remain permanent if they were not being followed.

Evaluation Criteria

03

02

05

EXPERIMENTATION

LLK

A The goal of the project was very explicit, to create a pattern, and so experimentation beyond creating a self-organising pattern with stigmergy was minimal.

Using Processing, a series of agents behaviours were tested and trialed:

CH03 | Experimentation

068 Figure 3.30 A: Control Simulation - the outcome by which other results were compared against

Experimentation

03

LLK

02

05

B

069

C

Figure 3.31 B: Detection Simulation altered from 60 units to 150 units C: Detection Width - altered from 40 units to 90 units

CH03 | Experimentation

Experimentation

03

02

05

D

LLK

E

070 Figure 3.32 D: Environment Size - altered from 400 x 400 units to 800 x 800 units E: Number of Agents - altered from 5 of each type to 10 of each type

CH03 | Experimentation

Experimentation

03

02

06

OUTCOMES

LLK 071

Through rigorous experimentation within Processing to create a stigmergic pattern, a final outcome was reached. The success of the outcome is based on its accordance with the intial conditions of the design task. In effect, this is an outcome that involves three types of agents that interact and influence one another to achieve a pattern of flexibility via manipulation by agents.

The following information shows exracts of the Processing script that effectively influenced the agents and their consequent pattern formation.

Aspects of cohesion, detection, distance and radius, environment size and the number of agents were crucial towards achieving this final outcome.

CH03 | Experimentation

Outcomes

03

02

06

Cohesion

A

B

LLK 072 Figure 3.33 A: Count must be larger than 0 under final if statement B: Count must be larger than 1000 under final if statement

// 527 - 535, â&#x20AC;&#x153;agentâ&#x20AC;? tab // for (int i = swarm[n].size()-1 ; i >= 0 ; i--) { Agent other = (Agent) swarm[n].get(i); if (this != other) { if (loc.distanceToSquared(other.loc) < neighborDist) { sum.addSelf(other.loc); // Add location count++; } } } if (count > 0) { sum.scaleSelf(1.0/count); return steer(sum,false); } CH03 | Experimentation

Cohesion is found by calculating the average location of nearby agents. If the result of that calculation is found to be that the agent is not currently in the average position, it will steer towards it.

Outcomes

03

02

06

Detection, Distance + Radius

A

LLK

B

C

Detection, Distance + Radius

073 Figure 3.34 Maximum range, minimum angle and maximum angle values for Detection, Distance and Radius

// 93 - 104, “agent” tab // if (isTarget) { maxRange = 100; minAngle = 0; maxAngle = 360; } else { maxRange = 60; minAngle = 0; maxAngle = 40; } CH03 | Experimentation

This section defines the detection range under “maxRange” and the cone of detection as being between the values set by “min_angle” and “max angle”. This is done firstly for the targets as a long range global attraction from all sides, and the for any other objects (such as trails created by agents), second, as a short and narrow range of detection.

Outcomes

03

02

06

A

B

C

For Targets Max range value set to 100 Min angle value set to 0 Max angle value set to 360

For Targets Max range value set to 200 Min angle value set to 0 Max angle value set to 360

For Targets Max range value set to 100 Min angle value set to 0 Max angle value set to 360

For Other Objects Max range value set to 60 Min angle value set to 0 Max angle value set to 40

For Other Objects Max range value set to 60 Min angle value set to 0 Max angle value set to 140

For Other Objects Max range value set to 60 Min angle value set to 0 Max angle value set to 120

CH03 | Experimentation

LLK 074

Outcomes

03

02

06

Environment Size

A

B

C

LLK 075 Figure 3.35 A: RES value set to 20, GRID_ SIZE value set to 20 B: RES value set to 40, GRID_ SIZE value set to 10 C: RES value set to 80, GRID SIZE value set to 5

// 36, “stigmergy” tab // size(RES*GRID_SIZE,RES*GRID_SIZE);

CH03 | Experimentation

The simulation is divided into a grid, and therefore the size of the environment is found by multiplying the value for the size of an individual cell by the value for the number of cells, which have been defined as “RES” and “GRID_SIZE”, respectively.

Outcomes

03

02

06

Number of Agents

A

B

C

LLK 076 Figure 3.36 A: NUM_ AGENTS value set to 3 for all types B: NUM_ AGENTS value set to 5 for all types

// 83 - 89, “stigmergy” tab // for(int i = 0; i < NUM_SWARM; i++) { for (int j = 0; j < NUM_AGENTS; j++) { Vec3D agLoc = new Vec3D(random(RES*GRID_SIZE), random (RES*GRID_SIZE),0);

This function defines the starting point of the agents as a random grid cell, and then uses the values determined under “NUM_AGENTS” to create a string of information under “boid”, which serves as the list of agents.

C: NUM_ AGENTS value set to 10 for all types

Agent boid = new Agent(agLoc); swarm[i].add(boid); } }

CH03 | Experimentation

Outcomes

03

02

07

CONCLUSION

LLK 077

Each goal set out at the beginning of the project has been achieved, and the main task prescribed of this project has been objectively fulfilled. The agents actively seek out nodes, leave trails and comprehend trails to generate a pattern.

Figure 3.37 A sequence showing the final pattern determined by stigmergic behaviour

However, it should be noted that while a pattern has been created, itâ&#x20AC;&#x2122;s applications are limited, and a significantly more complex solution would be required to respond to a more specific problem.

CH03 | Experimentation

Conclusion

03

02

07

LLK 078 Figure 3.38 The final image taken of the sequence

CH03 | Experimentation

Conclusion

03

03

00

EMERGENT SYSTEMS

LLK 079

The literature review provided a comprehensive overview of how to structure a system design problem, in addition to a number of commonalities shared by successful design systems: | Holistic The design process is used a multi-disciplinary approach to solving the problem to ensure all of the issues presented are addressed.

Figure 3.39 Synthesis, Analysis, Evaluation, Optimisation and Constraints + Parameters diagram showing the evolution of solutions

|Iterations The design process is iterative, continual evaluation allows the system to be constant improved by systematic changes and removal of weaknesses.

CH03 | Experimentation

Emergent Systems

03

03

01

INTENT + CONDITIONS

LLK 080

Task The aim of this project was to design a system which responds to the needs of a given site and intensifies the surrounding programs. As with the previous two projects, a continual process of simulation, evaluation and optimisation would again be followed to reach the most effective solution to the task at hand.

Figure 3.40 Agent-based system determining the most effective circulation

Intent To address the task, an agent-based system was to be created in order to determine the most effective circulation routes based on the type of people visiting the site, and the physical layout of the site as it is in its current state.

CH03 | Experimentation

Intent + Conditions

03

03

01

1 : 5000 N

LLK 081

Conditions The outcome needs to satisfy the following four rules:

Figure 3.41 Site: Queen Victoria Market car park

| Location The system must be located at Queen Victoria Market (Lat. -37° 48’ 3.27”, Lon. +144° 57’ 24.19”) | Environmental Limitation The system must fit within 150x100m | Intensify Existing Site The system must contribute to the intensification of activities on and around the site | Induce Evolution of Existing Activities Must introduce new activities that stem from existing qualities

CH03 | Experimentation

Intent + Conditions

03

03

02

METHODOLOGY

LLK 082

The main objective of this project was to produce an efficient and realistic set of paths for circulation on the site by encoding a number of agents with the behavioural qualities of the anticipated users of the space. As such, two separate agent types were required to populate the site: | Tourists Agent capable of reading existing paths, but not creating traceable paths, which move slowly between a set of predetermined access points. | Locals Agents capable of reading existing paths in addition to creating new paths which move quickly between a predetermined set of access points.

| Access Points The points at which the agents spawn from and move between. | Site Boundary The area in which the agents were set to work within. | Roads + Visual Cues Paths in and around the site which could not be intercepted, instead requiring the agent types to be able to move in the z-axis before passing over them.

In addition, the agents needed to be able to respond to a number of additional environmental factors encoded into the simulation environment: CH03 | Experimentation

Methodology

03

1 : 5000 N

LLK

03

02

1 : 5000 N

083 Figure 3.42 Site Conditions: Access

CH03 | Experimentation

Figure 3.43 Site Conditions: Visual Connections

Methodology

03

03

02

LLK 084

In order to ensure the agents were forming the most optimal solution given the environmental conditions they worked within, a number of variables to their behaviour would be required to ensure a continual process of simulation and optimisation would be possible: | Agent Speed As in the previous project, this affects the ability of an agent to correct its movement based on the acquisition and comprehension of new information.

| Population Size The number of types of agent to populate the sketch, required in order to appropriately simulate the site to create paths for the right number of people and right number of programs. | Limit to Z-axis Movement Without a height restriction, there was a possibility of the agents continuing in the z-axis without stopping, so one is required in order to ensure the simulation is realistic.

| Cohesion Strength The ability for an agent to read and steer towards an existing path if determined to be the most efficient path.

CH03 | Experimentation

Methodology

03

03

03

AGENT LOCAL

METHOD DESCRIPTION

TOURIST

Find Node LLK

Is there an existing path?

085

Figure 3.44

YES

Flowchart of the basic logic of the agents

NO

NO Was the path left by a local?

YES YES

Create traceable path

Create untraceable path

Does the new path intersect an existing path?

NO

Create new path + re-evaluate intersection

Follow path to node

CH03 | Experimentation

Method Description

03

03

03

AGENT

Has a line been encountered?

LLK

NO

YES

086 Figure 3.45

Jump over line

Continue as normal

Flowchart outlining the logic of the agents in regards to environmental influences

Has the agent past line attraction distance?

CH03 | Experimentation

NO

YES

Continue until past attraction distance

Come back down to ground level Method Description

03

03

03

LLK 087

All of the agents adopt a basic logic in the simulation.

It is this behaviour that governs the basic movement of the agents between access points, and allows them to circulate the site accordingly.

Additionally, the agents need to avoid obstacles.

This is crucial to the overall circulation, as it allows agents to navigate in the z-axis to avoid otherwise binding obstacles, resulting in a three-dimensional set of trails.

CH03 | Experimentation

Method Description

03

03

04

EVALUATION CRITERIA

LLK 088

The evaluative criteria primarily relied on replicating the basic behaviour as set out at the beginning of the project. These points were evaluated as either successes or failures: | Agents move between access points | Multiple agent types exist with different types of behaviour | Agents are capable of leaving trails | Agents are capable of comprehending and re-orienting their trajectories to follow trails | Agents are capable of comprehending avoiding objects by moving in the z-axis

CH03 | Experimentation

Quality The solution was not something which could be measured on a continuous quantitative scale. As such, assessing this was rather difficult, and was done by exporting the results at set intervals of 20 seconds for a simulation running only the tourist agent, a simulation running only the local agent and finally a simulation running both agents together.

Using these sets of information, test programs were set out on the site to see how naturally they could be integrated. From here, it was possible to check to see if there were enough points of regular traffic and interaction between the different types of agent, in addition to having enough traffic of single agent types to fit the programs required. This gave the possibility of changing variables such as population size, cohesion factor and agent velocity to produce different results in order to find the most optimal solution.

Evaluation Criteria

03

03

05

EXPERIMENTATION

LLK 089

Experimentation with the system was done through the four (4) step method discovered through the Alfaris and Merelloâ&#x20AC;&#x2122;s literature review: | Synthesis The system would be run using a set of given parameters.

| Optimisation Variables and behaviour are changed based on the analysis and evaluation stage prior to re-synthesising the simulation and starting the process again.

This process is followed until an optimal outcome is reached.

| Analysis Once the simulation reached a point of maturity, it was checked to see if it met the goals of the system. | Evaluation The resulting simulation is compared to the evaluated criteria set out previously, noting the performance of each variable and the degree to which it is successful.

CH03 | Experimentation

Experimentation

03

03

05

Access Points

LLK A

B

090

C Figure 3.46 A: -70, 45 0, 0 70, 45 70, -45 -131.5, -93.5 -70, -45

// 436 - 439 (b2/local) // if (d2[i]<5) tgt_n2[i]=int(random(tgt2.size())); stroke(blCol); fill(blCol, 2); if (trail2) for (int n=0;n<tgt2.size();n++) ellipse(tgt2.get(n).x, tgt2. get(n).y, 5, 5);

This statement specifies that the agents seek out the coordinates specified under â&#x20AC;&#x153;tgt2â&#x20AC;?, which are defined under an external text file.

CH03 | Experimentation

The access points were placed by finding the most critical areas of the site. These were the entrance points, the centre of the site and the edge of flagstaff gardens. These access points allowed the broadest circulation which made the best use of the site possible.

Chosen access points: Simulation A (-70, 45), (0, 0), (70, 45), (70, -45), (-131.5, -93.5), (-70, -45) centre of the site is the relative origin

B: -30, 35 30, 0 0, 35 30, -35 -131.5, -95.5 -30, -35 C: -11, 22 33, 44 55, 44 33, -22 -131.5, -93.5 -11, 0

Experimentation

03

03

05

Velocity

LLK 091

40

85

200

Figure 3.47 L to R: Agent speed set to 0.40 Agent speed set to 0.85 Agent speed set to 2.00

// 441 (b2/local) // pa.setMaxspeed(0.85);

As a function, “pa.setMaxspeed” sets the initial velocity of the agents being referred to under the current “pa” statement.

CH03 | Experimentation

Primarily determined by realism. The chosen values allowed the agents to move fast enough to resist the pull of other trails to some degree, but also slow enough that they could re-orient themselves quickly I response to reaching an object or reevaluating their objective. Additionally, the goals of the site users were taken into account, resulting in a faster speed chosen for locals than tourists due to their familiarity with the site.

Chosen values: 85 for locals, 75 for tourists Experimentation

03

03

05

Cohesion Distance

LLK 1

15

092

150 Figure 3.48 L to R: Cohesion vaule set to 1 Cohesion value set to 15 Cohesion value set to 150

// 502 - 506 (b2/local) // for (int n=0;n<pop2;n++) { cps2[i][n]=b2.get(i).closestNormalToSpline(sp2[n], futureLoc(15));

b2.get(i).

splTarget2[i]=cps2[i][0];

Once again, determined by realism. A short detection range meant that the agents would often not notice trails left by other agents, while a long detection distance allowed for no exploration beyond existing paths.

}

For a given population (“pop2”, in the case), this statement calculates the closest normal to a trail left by another agent within a certain distance, which is specified under “futureLoc”.

CH03 | Experimentation

Chosen value: 15

Experimentation

03

03

05

Cohesion Factor

LLK 093

1

15

150

Figure 3.49 L to R: Cohesion factor value set to 1 Cohesion factor value set to 15 Cohesion value factor set to 150

// 507 - 511 (b2/local) // if (frameCount>35) {

// future Loc 15

for (int n=1;n<cps2.length;n++) for (int ii=0;ii<b2.size();ii++) { if (b2.get(ii).futureLoc(15).distanceTo(splTarget2[ii])>b2.get (i).futureLoc(15).distanceTo(cps2[ii][n])) splTarget2[ii]=cps2[i][n]; }

Similar to the cohesion distance, having a low factor value meant that agents would steer towards existing trails at the first sign of detection, and a high factor meant that they would reach their destination before finally aligning themselves to the existing path.

} Under this statement, the agent will steer towards the closest normal of trail when detected so that it is at the end of the detection range.

CH03 | Experimentation

Chosen value: 15

Experimentation

03

03

05

Jumping

LLK Disabled

094

Enabled Figure 3.50 L to R: Obstacle hopping disabled Obstacle hopping enabled

// 480 - 485 (b2local) // for (int ii=1;ii<int(listLengthRv[0]);ii++) for (int iii=int(listLengthRv[0]) ;iii<int(listLengthRv[0])+int(listLengthRv[1]);iii++) {

This allowed the agents to safely maneuver obstacles such as roads.

if (min(Drd)<max_d || (pafutloc.x>=ptsRv[iii].x && pafutloc. x<=ptsRv[iii-1].x && pafutloc.y>((((ptsRv[iii].y-ptsRv[iii-1].y)/ (ptsRv[iii].x-ptsRv[iii-1].x))*pafutloc.x)+(ptsRv[iii-1].y-(((ptsRv[iii].yptsRv[iii-1].y)/(ptsRv[iii].x-ptsRv[iii-1].x))*ptsRv[iii-1].x))))) { if (pa.getLocation().z<max_h) pa.setVelocity(veloUp); }

else if (pa.getLocation().z>0) pa.setVelocity(veloDn); }

This statement is somewhat convoluted as it needs to consider objects defined under “river”. If any objects are within their detection range, the agents will accelerate in the z-axis.

CH03 | Experimentation

Jumping implemented into script.

Experimentation

03

03

05

Maximum Height

LLK 095

5

10

40

Figure 3.51 L to R: Height restriction value set to 5 Height restriction value set to 20 Height restriction value set to 40

// 482 (b2/local) // if (pa.getLocation().z<max_h) pa.setVelocity(veloUp);

Once again, “max_h” is number defined elsewhere in the script. This part of the script says that if the z-coordinate of the agent is currently below the value defined under “max_h” when it is still within detection range of an object defined under “river”, it will continue to rise. Once the z-coordinate is equal to the number defined under “max_h”, it will no longer rise.

CH03 | Experimentation

This related to the gradient that was created when moving in the z-direction, as the paths needed to be at a traversable slope.

Chosen value: 10 Experimentation

03

03

05

Number of Agents

LLK 3

10

096

30 Figure 3.52 L to R: Population set to 3 for both agent types Population set to 10 for both agent types Population set to 30 for both agent types

// 249 - 257 (b2/local) // for (int i=0;i<pop2;i++) { Vec3D sloc=new Vec3D(tgt2.get(int(random(tgt2.size())))); Ple_Agent pa=new Ple_Agent(this, sloc); Vec3D sum=new Vec3D(); int c=0; for (int ii=0;ii<tgt2.size();ii++) { sum.addSelf(tgt2.get(ii)); c+=1; } “pop2” defines the number of particular type of agents to be run in a simulation. In this case, “pop 2” refers to a number predetermined value, but is only now used to generate the number of agents.

CH03 | Experimentation

Chosen by estimating the target and predicted number of site occupants.

Chosen value: 60 of each agent type.

Experimentation

03

03

05

Trail Length

LLK 097

10

100

1000

Figure 3.53 L to R: Trails of 10 units Trails of 100 units Trails of 1000 units

// 261 - 262 (b2/local) // pa.initTail(1000); b2.add(pa);

“initTail” tells the simulation to leave a trail with a length of 1000 onscreen units, which is defined under “b2.add(pa)”.

CH03 | Experimentation

Essentially, this was how long the paths would be readable for. Higher values meant more permanent and consistent paths, which fit with the aim of the project.

Chosen value: 1000 Experimentation

03

02

06

OUTCOMES

LLK 098

Each of the objectives set out in the initial planning phase have been achieved. Specifically, the solution created is capable of: 1_Using multiple interacting agent types in the same simulation which separate variable controls for each type: | Agent speed | Agent cohesion strength | Agent population | Z-axis movement restriction 2_Introducing different objects into the testing environment which evoke different behavioural responses form the agents:

CH03 | Experimentation

| “road” Roads and pre-existing visual cues – the agents will move in the z-direction to pass these obstacles. | “river” Used to determine the site boundary – agents do not cross these boundaries unless they are specifically doing so to reach an access point on the other side. | “tgt” The access points which the agents move between – agents also spawn from these points.

Most importantly, the agents are capable of both leaving and following trails, as is required of a stigmergy system.

Outcomes

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Figure 3.54 Tourst simulation at twenty second (20s) intervals

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Figure 3.55 Simulation of local agents at twenty second (20s) intervals

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Figure 3.56 Simulation of both tourists and locals at twenty second (20s) intervals

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The overall findings are consistent with the intentions of the brief. A very well-articulated set of paths have been created, which allows the architectural form to be developed with a clear sense of direction and requirements

The results produced have left clear cues which can be analysed to determine programmatic placement. This is a very important goal to the simulation, as it allows circulation around the site to be smooth and structured without specifically locking occupants into any one path.

Certain results of running the simulation within a set of controlled parameters have been unexpected, but useful outcomes for the design, such as: | Increased traffic at the NW corner of the main site at the beginning of the simulation

CH03 | Experimentation

Conclusion

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Conclusion

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Figure 3.58 Local and Tourist paths interacting with one another

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Conclusion

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ARCHITECTURAL DESIGN

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Queen Victoria Market Corner of Victoria Street and Elizabeth Street, Melbourne, Australia | Lat. -37° 48’ 3.27”, Lon. +144° 57’ 24.19”

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Architectural Design

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Design Task The purpose of this project is to propose and implement a design system into an architectural design. The site is situated at the current car park of Queen Victoria Market (QVM), Melbourne, Australia. The architectural design must be within the constraints of 150x100m while contributuing to the intensification of activities not only on, but also around the site. As a result, new activities that stem from existing qualities must be considered in the final design.

Figure 4.0 Rules: location, dimensions, activity intensification, new from existing

1 | Must Be Located at Queen Victoria Market 2 | Must Be Within 150m x 100m 3 | Must Contribute to the Intensification of Activities On and Around the Site 4 |Must Introduce New Activities That Stem From Existing Qualities CH04 | Design

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Figure 4.1 Site: Queen Victoria Carpark showing the design task rules

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Melbourne CBD highlighting the potential for Queen Victoria Market to become an space for informal social activities

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The design intent for the architectural design is diven by analysis of the following:

Melbourne Central

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Federation Square

CULTURAL

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Context In the CBD, Federation Square and Melbourne Central are the public spaces that define the city of Melbourne. Federation Square exhibits a strong sense of Melbourne culture, global culture and the Arts. Melbourne Central, on the other hand, concerns itself with economics through a successful shopping district. Both of these areas incorporate social actvities, however it is only secondary to culture and economics, respectively.

As a result, the existing social qualities of Queen Victoria Market should be intensified to create a informal social space within the CBD. As a result, the site will have similar touristic qualities to that of Federation Square and Melbourne Central, but differ in its intent. Federation Square will remain as a cultural hub of exhibition, Melbourne Central as an economic district for purchase, and Queen Victoria Market will become the informal social hub for interaction.

Design Intent

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Figure 4.3 Delineation between Locals and Tourists to the present day as a result of the change in the marketâ&#x20AC;&#x2122;s purpose.

PAST

Past to Present The need for a social space is highlighted by the change in use of the site. Originally, when Queen Victoria Market first opened at its current CBD location in 1857 the main focus of the markets was on locals and produce. Even now, locals still visit the markets due to their varietal offerings of fruits, vegetables, meat, poultry, seafood and gourmet foods. The market has also extended into nonfood related stalls, such as the sales of clothing, handmade arts and crafts and plenty of souvenirs for visiting tourists. The market has shifted in its purpose, from a place of purchase and trade to a centre that is driven by social and touristic motives.

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Wasted Potential The markets do not successfully meet the needs of the users. Tourists who visit the site are socially delineated from the locals due to the limited interaction from programs. Queen Victoria Market does not meet its full potential as a tourist attraction.

Hours of Operation People are only attracted to the site when the market is open. On Mondays and Wednesdays when the market is closed and completely inactive and on all other days, the trading hours only last until four oâ&#x20AC;&#x2122;clock in the arfternoon. The hours of inactivity make the site uninviting and it appears like a â&#x20AC;&#x2DC;ghost-townâ&#x20AC;&#x2122;. These shortened hours of operation place restrictions on who can use the site, rather than being accessible to the wider set of users.

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Figure 4.4 Top Left (A) + Right (B): show the limited attractions for tourists at the current site Figure 4.5 Bottom Left (A)+ Right (B): reveals a significant contrast between the times when the market is open and active (left) and closed and like a ghosttown (right)

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Disconnection Flagstaff Gardens, also considered a place of interest for tourists, is a space that reflects upon the history and culture of Melbourne. However, William and Peel Streets create a disconnection between these two sites, reducing the potential for tourist intensification and connection.

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Design Intent

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Figure 4.6 The existing disconnection between Flagstaff Gardens and Queen Victoria Market, along William and Peel Streets

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Design Intent

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Intensification + Interaction The site analysis, summarised by the following headings, have influences the design intent: | Delineation between Locals + Tourists due to Programs + Purpose |Queen Victoria Market Does Not Meet Its Full Potential as a Tourist Destination | People are Only Attracted to the Site in the Limited Hours That the Market Operates |Disconnection Between Places of Interest

CH04 | Design

Design Intent The design intent is to complement Queen Victoria Market through the intensification of tourist activities, while maintaining the site as a local area, therby facilitating interaction between locals and tourists.

The issues of delineation, tourist attraction potential and hours of operation are all influenced by the architectural programs of the site. However, in order to resolve the issue a disconnection between Queen Victoria Market and Flagstaff Gardens, more information that is specific to the physical site must be collated. Additionally, an understanding of how people approach and access the site is crucial to intensifying the numbers of locals and tourists.

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Figure 4.7 Design Intent: Intensification of tourists by creating a places of interest, plus maintaining the site as a local area accomodates a space for maximised interaction between the two parties

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Program types and their purposes

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Figure 4.9

GENERAL MERCHANDISE

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The extension of the program types and their purposes into specific activities

FISH POND

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WATER SQUIRT

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To intensify the site as a tourist destination, as well as a local area, a range of new and complementary programs are added to the market. Activities are interactive and many are unsupervised so that these activites are accessible at all hours, even when the market is closed. This is achieved through the following four (4) program types: | Acquiring Type 1: Primary programs for local use Purpose: Maintain and increase the local users on the site. | Discovering Type 2: Secondary program that is primarily for tourists, but sometimes attracts locals as well Purpose: Maintain and increase tourists on the site.

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| Observing Type 3: Spaces for activities that demonstrate, elaborate and support the ‘Acquiring’ and ‘Discovering’ programmes Purpose: Increase both locals and tourists on the site allowing for an opportunity to see more of Melbourne’s social and cultural side. | Engaging + Learning Type 4: Programs that consist of engaging activities where users learn about the Acquiring (locals) and Discovering (tourists) programs Purpose: Increase the interaction of locals and tourists through activities. This extends on the ‘Observing’ program, allowing for actual creation rather than just observation.

In line with the program type, there are specific programs allocated to fufil the purposes, with a list of specific activities that occur within that program type: Design Intent

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Figure 4.10 ‘Aquiring’ programs associated with local use, and the available activities Figure 4.11 ‘Discovering’ programs predominantly for tourist use, but not excluding local use, and the associated available activities

GLOBAL FOOD HUB

NATIVE AMPHIBIANS, INVERTEBRATES + REPTILES GIFT SHOP

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INCREASE TOURIST USE + LOCAL USE

OPEN TEMPORARY SPACE

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ZINE FAIR PUBLIC CANVAS

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ENGAGING + LEARNING INTERACTIVE LEARNING FOR TOURIST USE + LOCAL USE

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Each of the programs aim to encapsulate the needs and desires of the intended user. In the case of locals, they are comparitively direct in their behaviour and more familair with the area and so will visit the General Merchandising, Mind Body Soul for morning tai chi or yoga sessions, along with Food Services to cater for the people of the nearby business district, as they walk through Flagstaff Gardens at meal times, to the site.

Tourists on the otherhand would be interested by aspects that are more unique to Melbourne. Such as Artisan Stalls, Speciality Foods as well as the Creepy Crawlies space that houses a variety of Australian frogs, insects and small reptiles.

Numerous Open Temporary Spaces allow for the display of visual arts and live performances and talks appealing to both tourists and locals. As well, the Melbourne Underground, which includes the sale of locally made zines, as well as a public canvas which encourgaes the public to artistically express

CH04 | Design

themselves on a dedicated surface. Replicating the vibe of Melbourneâ&#x20AC;&#x2122;s street art seen in laneways of the CBD. In order to actively engage and learn, Workshops are provided. The Hands On workshop consists of arts and crafts sessions to complement the Melbourne Underground scene. Similarly, the Permaculture Nursery acts as a leraning centre where people learn about how the fruits and vegetables and other local produce sold at the market are grown. The Native Park is a leisurely space to facilitate social interaction, in a space where people, similar to that of a botanical garden, can learn about the flora and fauna species. These three program types are vital to the success of engagement and learning of tourists and locals.

The precise behaviours and level of engagement that this would encourage for both locals and tourists was analysed to determine these â&#x20AC;&#x2DC;Engaging + Learningâ&#x20AC;&#x2122; programs, as seen in Figure 4.14.

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The behaviour of the tourists and locals influence the types of engagement influencing the programs

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SITE ANALYSIS

Site Flagstaff Gardens 1 : 5000 N

LLK 125 Figure 4.15 Site analysis showing the prominent access points to the site, visual connections beyond and within the site, as well as Flagstaff Gardens connection information

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The additional site information that was required in order to apply the system, is as follows: Access The use and access of a site can influence the location of programs. In this analysis, pedestrian foot traffic was observed with the most signifcant points of access being from the four corners of the site. These are the points at which the user was most likely to first contact the site. Each access point correlates to a method of transportation, whether it be car parking externally to the site (South-West) or whether it be a short distanceâ&#x20AC;&#x2122;s walk from a tram stop, such as the Elizabeth Street Queen Victoria Market tram stop. Pedestrians also access from Flagstaff Gardens, however due to a disconnection across the main roads of William and Peel Streets, and the location of pedestrian crossings, access to the site is directed towards the South-West corner of the site.

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Height For a connection to be formed between Flagstaff Gardens and the site, a form created over Peel and William Streets would have to be considerate of the heavy traffic flows. The average height of pedestrian overpasses in Melbourneâ&#x20AC;&#x2122;s CBD is 5.2 metres (5.2m), with a minimum required height of 4.4 metres (4.4m), according to standards.

Visual Connections At particular points of the site, there are views which maintain a connection to the surroundings.

Two parallel views are created through from the markets into the car park, and vice-versa. While an external visual connection exists through the site into the markets from Dudley Street. These visual connections should be kept clear in order to design a form that does not intrude on existing connections.

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The intent and site information instigated the need to understand how people circulate through the site.

In order to simulate the movement of people, the system created through stigmergy, as explained in 03.00.00 of this text, was used for the architectural design process.

Emergent systems, as a entirety, are able to consider the effects of an evolving society and changing needs. A city is a dynamic and adaptive system based on the interactions with its users, informational feedback loops, pattern recognition and indirect control (ProtoRaptor, 2012), emergent systems are required due to their ability to solve complex and conflicting issues. To satisfy the architectural design intent, the system was used to determine the following, based on the circulation patterns of the locals and the tourists: | Allocation of Spaces | Size of Spaces | Connections of Programs CH04 | Design

The resulting system attempts to interpret the urban condition based on its inputs, in this case the behaviour of locals and tourists as well as the environmental influences.

This system draws upon ant foraging in nature.

Through this process, the most efficient method of path optimisation is discovered. The ants search for a food source and upon success return to the nest to store the food, then repeat this process seemingly along a similar path. During this process the ants leave trails of pheronomes to leave traces of their presence. The path of strongest pheronome will be followed due to inherent behaviours. A similar process can be used to understand the circulation of people on the site.

This system simulates circulation based upon the embedded information of the agents, creating a three-dimensional journey for the visitors through a connected network of programs. These connections are explained through Figures 4.16 - 4.22:

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Connection of programs: Locals use ‘Acquiring’ programs, and occassionally the ‘Discovering’ programs, predominantly used by locals.

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These programs are observed by the ‘Observing’ program, which must be near the ‘Engaging + Learning’ programs to facilitate interaction between the two agents.

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Figure 4.17 The programs associated with each program type

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130 Figure 4.18 The programs and their associated activities for each program type

LOCAL ART + DESIGN

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PUBLIC CANVAS

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Example of the connection between program types from a food and nature perspective

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Example of the connection between program types from a coffee -centred focus

LOCAL PRODUCE

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LLK 133 Figure 4.21 Example of the connection between programs based on art and the arts

HANDS ON

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134 Figure 4.22 Example of the connection between program types with programs concerning the body and performance

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Program Allocation The stigmergic simulation is run to determine the allocation, space and connection of programs for the architectural project. The allocation is based on the inital information of the system, specifically that of the local and tourist agents, including the number of agents, speed and strength of attraction. Additonally, entry and exit points to the site influence the circulation and concentrations of people, determined by the inital access points of the system.

Type 1 - Acquiring This program allocation is determined by the circulation of locals. Once the circulation of the locals has been generated, the â&#x20AC;&#x2DC;Acquiringâ&#x20AC;&#x2122;, localsonly programs can be situated along these specific paths.

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Method Application

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Figure 4.23 Program allocation of type 1: ‘Acquiring’

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Type 2 - Discovering Once the local circulation has been defined, along with the complementary programs, the next step is to determine where the tourists move. In some cases tourists travel among themselves, and in other cases they circulate with the locals. Due to this, both local and tourist circulations are used to determine to location of type 2 - â&#x20AC;&#x2DC;Disocveringâ&#x20AC;&#x2122; programs. The programs are situated along the paths where there are solely tourists, and paths in which there is a dominant strength of tourists with only a minimal presence of local paths.

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Method Application

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Figure 4.24 Program allocation of type 2: ‘Discovering’

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Type 3 - Observing Program type 3 - ‘Observing’, results from the increase in both tourists and locals. In the Processing sketch, this is indicated by paths that have a relatively equal strength of local and tourist paths. The location of these programs must also be relative to that of type 1 - ‘Acquiring’ and type 2 ‘Discovering’ to fufil its purpose. A visual connection between these programs is possible due to a threedimensional form, with type 3 having the ability to be up at a higher level looking down on the other programs for a broader observation.

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Type 4 - Engaging + Learning The allocation of type 4 - ‘Engaging + Learning’ is the final phase of program allocation, due to the programs dependence on all other program types.

The program must be next to type 3 - ‘Observing’, so people are inspired by what they see (‘Observing’) and then can actively engage in that activity (‘Engaging + Learning’). Additionally, people already engaging in a workshop, for example, of the ‘Engaging + Learning’ activities should be inspired, through a visual connection, to the people who are creating the objects that they are learning about and making themselves (‘Observing’).

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Figure 4.25 Program allocation of type 3 and type 4: ‘Observing’ and ‘Engaging + Learning’

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Method Application

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Figure 4.25 The architectural design is based directly on the paths created by the circulation of tourists and locals

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Method Application

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Figure 4.27 Final architectural design on the site

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Essentially, the purpose of the agent-based system was to effectively determine the allocation of all of the program types ensuring that they were connected to fufil their inital purposes.

Generative System - Tessellations With the conclusion of the final program allocation, the architectural surface was created as a direct result of the this three-dimensional framework, through the application of a generative system in Grasshopper. The surface utilises a tessellation material system. In order to emphasise the connectivity between programs visually, openings were generated within these tessellations. The tessellations create the openings based upon not only visual connectivity, but the natural light and air and acoustic requirements of the programs.

CH04 | Design

The line of sight that connects, for example, type 3 - ‘Observing’ programs with type 1 - ‘Acquiring’ and type 2 - ‘Discovering’ programs requires openings. Two types of openings can be used in this scenario: a glazed opening and openings that are full perforations of the skin, with no glazing.

The specific type of opening will depend on the program itself. Programs such as the Public Canvas require more solid surface relative to the openings to allow for the painting of a greater surafce of the literal walls of the structure. This contrasts to the requirments of the Native Park area which would require a larger quantity and volume of openings, especially those without glazing. As a result, the Native Park allows for increased natural light and air to penetrate to the gardens and people experiencing the space within.

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LLK 144 Figure 4.28 Architectural sketch design showing the integration of both glazed openings and full perforations of the skin.

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Method Application

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OUTCOMES

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The final architectural outcome is an efficient circulation path based on the interaction between the locals and tourists. Each path services either only locals, only tourists, or both. This is based on the types of pheromone trails left behind throughout the simulation of people circulation on the site. The resulting paths then create spaces for circulation and also space for programmes depending on the pheromone strength. The paths themselves create volumes which are specifically informed by the amount of traffic that goes through them, and in some cases involving the splitting and merging of internal volumes. The design of paths is driven by the strength of the pheromone trails, hence it is a direct representation of the stigmergy system based on the initial design inputs and intent.

CH04 | Design

The programmes are successfully allocated based on connections and relationships between types 1, 2, 3, 4: ‘Acquiring’, ‘Discovering’, ‘Observing’ and ‘Engaging + Learning’. It is these programs and their relationships that facilitate interactions through learning and engagement between locals and tourists, creating an informal social space within Melbourne.

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LLK 147 Figure 4.29 Final program allocation

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GENERAL G L M MER MERC HAND ANDISE SE S E MIIIND BOD M MIN DY SO S OUL O UL FOO OOD O OO O OD SERV ER ERV E RVICES ARTI ART A RTI RT TIISA T AN ST STAL S TAL LLS S SPEC SP S PE IIA IAL IALT A ALT AL LT L Y FOOD F FOO O S

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CREEPY CREE C REE E Y CR CRAW C RAW R WLIE LIES L IE OPEN OPE OP O PEN PE P E T EN TEM EM MP SP SPAC PAC P A ACE MELB BO OUR URN UR R E UNDE DE DERGR ERGROUND RG RGRO RG RGR GRO GR RO OUN UND U ND WORK W ORK KSHOP SHOP HOPS OP O PS PERMACU UL U LT TURE URE RE RE NURSERY Y NATI T VE TI VE PAR PARK PA P A

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LLK 149 Figure 4.30 Sectional cut through the paths

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LLK 151 Figure 4.31 Sectional cut through the paths

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LLK 153 Figure 4.32 Day view

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LLK 154 Figure 4.33 Night view

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LLK 155 Figure 4.34 Perspective: approaching the site

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LLK 157 Figure 4.35 Perspective showing the visual connections

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LLK 159 Figure 4.36 Interior perspective of program type 3 - ‘Observing’ and type 4 - ‘Engaging + Learning’.

The specific activities taking place is the ‘Public Canvas’ and the ‘Hands on’ arts and crafts workshop

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In conclusion, this project successfully provides an informal social space within Melbourne. By utilising agent-based systems of stigmergy and generative systems of tessellations, an architectural design that encapsulates an interactive atmosphere through the continous flow of activities along the paths is created. The optimal allocation of programs to enhance connected spaces and engagement, was defined by the system.

The agent-based system stigmergy could potentially be improved by introducing more detailed controls over the agent behaviour. For example, if an agentâ&#x20AC;&#x2122;s movement was restricted to moving upwards and downwards along a specific gradient, the final framework of the paths would not require any additional top-down alterations to meet building codes and regulations. This outcome would then be a pure representation of the system as it uses only bottom-up strategies of design.

CH04 | Design

In addition, an agentâ&#x20AC;&#x2122;s behaviour can be enhanced to create spaces for programmes based on the required area of a specific program. This behaviour in effect will strengthen the overall design in creating specific spaces that achieve optimal outcomes despite conflicting goals.

Slight changes to the system can potentially improve it such that it becomes a much more flexible and dynamic system that is responsive to changes. As a result, it would be able to adapt and self organise in an environment, thereby enhancing the emergent properties of the architectural design.

It would truely be a piece of architecture that can evolve with the changing needs of society.

Conclusion

LLK 162 Figure 4.37 Visual connections between programs are emphasised through the generation of threedimensional paths

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Conclusion

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REFERENCES

CH05 | References

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Weinstock, M, 2006 ‘Self-organisation and Material Constructions’, pp.

034 - 041

Hensel, M, Menges, A, 2004 ‘Emergence in Architecture’ pp. 006 - 009

Leach, N, 2009 ‘Digital Morphogenesis’, pp. 032 - 037

Alfarais, A, Merello, R, 2008 ‘The Generative Multi-Performance Design System’, pp. 448 - 457

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Hermann, C, 2010 ‘Proto Towers’, accessed from < http://www. christoph-hermann.com/generative-design/generative-design-prototowers/ >

Tan, L, 2013 ‘LA02 RESOURCES I - GENERATIVE SCRIPTS’ August 06, 2013, Presentation, University of Melbourne 031

021 - 022 Carranza, M, Coates, P 2000, ‘Swarm Modelling - The use of Swarm Intelligence to generate architectural form’, pp. 001 - 021

Rosenkrantz, J, 2013 ‘ Hyphae Lamps - An Infinite Series of Lighting Designs’ in Nervous System, accessed from <http://n-e-r-v-o-u-s.com/ blog/?p=1701>

Leach, N 2009, ‘Swarm Urbanism’, pp. 056 - 063

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ProtoRaptor 2012, ‘Precedent_Swarm Urbanism’, accessed from <http://www.protoraptor.com/2012/02/precedent-swarm-urbanismkokkugia.html>

Su, Q, Guo, S 2009 ‘Topological Silk Stretching’, accessed from < http://new-territories.com/000%20my%20web%20site/PlusN%20 los%20angeles/www.new-territories.com/blog/n4/index85da. html?page_id=65 >

CH05 | References

03.03.00 - 04.05.00 panD.a 2013, accessed from <http://a3panda.altervista.org/index. html>

References

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ILLUSTRATIONS

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EMERGENT SYSTEMS

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