STIGMERGIC SPACE University of British Columbia

AnnaLisa Meyboom; Dave Reeves STIGMERGIC SPACE University of British Columbia

1 Termite nest construction process

2 Wasp nest construction process

ABSTRACT This paper presents a multi-agent approach to space planning. Using the algorithm as a primary design tool, it posits to model an active site of programmable collective intelligence—one that is able to inform its own development internally. The mechanisms of self-organization from ants, termites, slime molds and other social organisms are examined and adapted to solve spatial adjacencies amongst elements of a given programmatic brief. Spatial organization becomes the emergent product of a competitive ecology. The task of space planning, one that is typically carried out by a singular high-level decision-maker (the architect, is approached through the distributed decision- making of low-level collective intelligence. This approach facilitates the design of a problem with high levels of complexity and competing requirements.

199 1 INTRODUCTION As architecture continues to interrogate the implications of the virtual realm and cybernetics, an investigation into a system of spatial “programming” that uses “programming” entirely to determine its own configuration is apt. This investigation has the architect designing the rules of the ecology of the system and allowing the agents within the system to play out the scenarios. The complex design problem posited is not solved by a single high-level decision maker but instead approached indirectly through the programming and deployment of many low-level decision-making entities acting in parallel. The system may be considered a cyber- netic ecology, which plays out based on the designers’ rules to provide multiple solutions. Self-organization refers to a broad range of formative processes amongst both living (biological) and non-living (physical) systems. 3 Ant colonies construction process Common to both is the nature by which these systems acquire their overall order and structure. In self-organizing systems, mac- The queen emits a primary pheromone, which creates a spatial roscopic order is arrived at through countless interactions internal template while each worker embeds a secondary pheromone in to the system — operation among its components at the micro- the building material which acts as an attractive stimulus. Figure 2 scopic level. There is no need for a centralized external source of illustrates the wasp nest construction process which is guided pri- control or comprehensive set of instructions. In fact, the individual marily by work in progress. Each wasp received building instruc- components themselves—be they fish or grains of sand—have tions from the local configuration of existing cells where certain no awareness of the complex global structures they collectively configurations act as stimuli for further construction. produce. They act autonomously on purely local information. Here, what might be perceived as “design intelligence” at the level Since being put forward by Grasse, stigmergy has been applied to of the whole is actually an emergent property of relatively non-in- explain nest building among many other social insects (Bonabeau, telligent interacting parts operate among non-living components. 1999: 15). While their respective architectures may differ, the forma- There is a critical difference between self-organization in physical tive processes of each are speculated to follow the same notion and biological systems. In physical systems, emergent proper- of indirect communication through modification of a shared spatial ties are in no way “beneficial” to the individual components that environment. In general, the architecture of colonial insects exists contribute to their production (Camazine et al. 2001: 13). In biological in a formative feedback loop with the population that occupies it. It systems, the autonomous interactions within the systems still simultaneously acts as an information cache—recording the auton- obey the fundamental laws of physics but are in a competitive omous decision-making of each individual as they locally alter its environment through which the advantage gets passed down configuration, and as a stimulus for its own development—provid- through evolution (Camazine et al. 2001: 13). ing information to trigger behavioral responses in the same group 2 STIGMERGY of individuals leading to further modification. At any given time dur- ing stigmergic processes, the current state of the spatial construct The indirect form of interaction in self-organized systems is more informs its future state. In this way architectural work-in-progress commonly referred to as “stigmergy”—a concept introduced becomes the source of subsequent “design decisions.” by French zoologist Pierre-Paul Grasse to explain task coordination and regulation in the context of termite nest construction The application of stigmergy has been incorporated into (Grasse 1954: 79). Grasse hypothesized that the coordination and the spatial design software, “Stigmergic Space Adjacency regulation of building activities do not depend on the workers Software” (SSAS) in which programmatic elements referred themselves but are mainly achieved by the nest structure. A stim- to as “agents” negotiate spatial territories through the depo- ulating configuration triggers the response of a termite worker, sition and detection of virtual “pheromones”. The pheromone transforming the configuration into another configuration that may deposition and sensing pattern is derived from insects such trigger in turn another (possibly different) action performed by the as ants and termites but the pheromones in the software de- same termite or any other worker in the colony. Figure 1 illustrates tect three-dimensional pheromones. Looking in more detail to the process of termites who rely on principles of pheromone insects’ use of pheromones, we can see that ants follow local detection and deposition to guide the construction of their nests. gradients in pheromone concentration tending towards regions of

INFORMATION ACADIA 2013 ADAPTIVE ARCHITECTURE 200 higher concentration while depositing additional pheromone in their wake for other ants to detect. Well-travelled paths attract more traffic, which further reinforce the pathways while weaker pathways diffuse and decay. For example as illustrated in Figure 3, ant colonies construct efficient path networks through the deposition and detection of pheromones. Each ant follows local gradients in pheromone concentration—tending towards regions of high concentration while depositing additional pheromone in its wake. Termites have a slightly more complex pheromone system in that the queen emits a primary pheromone, which acts as a spatial template, and the workers follow a specific gradient of concentration to begin working. The workers then embed a secondary pheromone in the building material, which acts as an attractive stimulus to other 4 Color space pheromone targets workers. Thus, pillars and walls that emerge as sites of material deposition become more likely sites of further construction. 3 SOFTWARE 3.1 Pheromones Programmatic relationships in the software are constructed by plotting each agent’s pheromone target in color space (RGB) (Figure 4). Agents with proximate pheromone targets will tend to coalesce over the course of the simulation. In this sense, programmatic com- patibility becomes a matter of color preference. Colors were chosen for pheromone values due to the three-dimensional nature of the target. A pheromone value represented by a single number has the disadvantage that it is on a one-dimensional number line and t. The depth of a three-dimensional space is not well represented 5 Algorithm in process where two distinct families of agents negotiate spatial territories by a number line therefore a more three dimensional method was chosen: colourspace was chosen as it seems more intuitive and therefore more easily understood. Further illustrated in Figure 5, two distinct families of agents negotiate spatial territories. One family has pheromone targets in the cyan range while the other prefers concentrations in the magenta range. Adjacencies form within each family but the two remain distinctly separate, occupying opposite corners of the bounding volume.

3.2 Nodes SSAS treats space as a volumetric array of discrete spatial units or “nodes” (Figure 6). The resolution and scale of this virtual environment depends upon the nature of the programmatic brief at hand. Each node within this matrix exists in one of two possible states—occupied or unoccupied. Nodes also hold references to their immediate neighbors allowing for the local exchange of information. Each node carries a pheromone value. Nodes participate in ongoing pheromone diffusion by averaging their own pheromone value with that of their neighbors. This allows pheromone levels to propagate through space and serve as 6 Nodes a communication medium amongst programmatic agents.

201 ANNALISA MEYBOOM; DAVE REEVES STIGMERGIC SPACE 4 AGENTS Programmatic elements are represented as autonomous agents whose goal is to territorialize some portion of the shared node network. An agent occupies nodes according to its desired pheromone value or “pheromone target”—preferring those with the smallest deviation (Figure 7). Once a node is occupied, its pheromone value tends towards its owner’s target— gradually saturating the local environment with 7 Nodes become pheremone sources the same concentration through diffusion. This allows adjacencies to form between compatible agents as they pick up each other’s “scent”. An agent occupies nodes by iteratively expanding its territory. At each step it considers any unoccupied node adjacent to its current territory for expansion. The node whose pheromone concentration is closest to the agent’s target is appropriated. If an agent has a sufficient number of nodes it will begin to refine its territory through contraction. Here, the agent releases the node with the largest pheromone deviation creating the opportunity to appropriate a different adjacent node in the next step. A node becomes a pheromone source when its pheromone concentration tends to a predefined value whether occupied or not. 8 Nodes become pheromone sources to create templates representing external This allows for the creation of various spatial templates to guide influences agent behavior in the event of external influences (Figure 8).

4.1 Use of Queen Pheromone Templates Pheromone sources can be set up to represent a variety of external influences. It is simply a matter of assigning certain meaning to certain pheromone concentrations (colors). In the sequence below (Figure 9), pheromone sources are introduced to the same agent population from Figure 5. Nodes along opposite faces of the bounding volume pull their own pheromone concentration towards white and black respectively. The gradient produced represents the transi- tion from public to private as imposed by a hypothetical context.

4.2 Use of Node Masking Node masking presents another means of templating external influences. Here, certain nodes are removed from the network in order to sculpt the environment. Agents can no longer occupy these removed 9 Queen pheremone template representing public and private affects the program distribution nodes nor do they participate in pheromone diffusion (Figure 10). 5 APPLICATION The application of SSAS was designed for spatial determination of a program in a building but is not limited to this application. The brief works for any spatial competition and works best when competition for space is tight. As a means of evaluation, this section applies SSAS to a hypothetical programmatic brief for a student residence at the Santa Fe Institute. Having drawn heavily upon the school’s existing body of research in developing the design tool, it presents itself as a natural venue for application. In order to ensure a certain complexity of spatial relationships, a diversity of programmatic elements are included in the brief (Figure 11 & 12). To be an effective 10 Examples of how node masking could be used.

INFORMATION ACADIA 2013 ADAPTIVE ARCHITECTURE 202 test case, it is critical that the brief be properly calibrated. Too sim- the bounding volume producing a gradient representing privacy. In ple and the solutions become predictable—not revealing compel- Figure 15, Scenario C, an atrium/circulation strategy is masked and ling alternatives to traditional design methodologies that motivate two types of pheromone sources are set along opposite faces of further investigation. Too complex and the solutions become im- the bounding volume producing a gradient representing privacy. penetrable—unable to be dissected and evaluated by the architect, The scenarios play out live while agents negotiate for space and essentially bringing development of the tool to a screeching halt. deposit and sense surrounding pheromones. A non-animated ex- The goal of this section is then twofold—to understand the tool’s ample of the run for scenario C is shown below: limitations in its current state and to propel further development. Results of the scenarios vary and depending on how they are The output of this test case is structured in three scenarios—each seeded, different outcomes are more or less successful. For with different degrees of external constraints. For each scenario, example, relative program sizes and their constraints can limit eight options are generated. Initial conditions – the starting po- agent movement and bind the agents into certain locations. sitions of programmatic entities—are unique for each option, “Discount factors” can be applied to agent movement for verti- giving a sense of the various equilibrium states the system tends cal movement, this constrains agents to prefer lateral to vertical towards. From here, the option that displays the most dominant movement. This allows for the difficulty of circulating vertically in tendencies is pulled apart in order to examine the resulting ad- physical architecture. Also, cohesiveness and shape can be ma- jacencies and spatial relationships. This process represents a single step in the ongoing feedback loop that propels the development of any form of goal-oriented collective intelligence. Based on the success of the emergent results—in this case the satisfaction of spatial adjacencies—the designer/programmer tunes the distributed decision-making routines responsible for their production. This is essential due to the non-linear nature of self-organizing systems—where the collective whole can not be inferred by simply examining the parts in isolation. Success or failure therefore, can only be assessed by letting the system play out ecologically. Only when this feedback loop is closed, can the problem solving potential of self-organization be leveraged within the domain of design—architectural or otherwise.

The three scenarios set up are illustrated in Figure 13 through 15: in Figure 13, Scenario A, a topography is masked and pheromone sources are set around the periphery representing exposure to 11 Pheromone values for programs natural light. In Figure 14, Scenario B, a topography is masked and two types of pheromone sources are set along opposite faces of

12 Pheromone values for programs with agent’s desired number of nodes

203 ANNALISA MEYBOOM; DAVE REEVES STIGMERGIC SPACE nipulated to prefer square shapes and tightly bound programmatic areas. Examples of outcomes for each scenario are illustrated below. These examples are only a single run based on a single seed condition, so many more variations have been observed. CONCLUSIONS & FUTURE RESEARCH Since its development, SSAS has also been applied not only to the examples illustrated but also to a high-rise building currently in design and in a Smart Geometry workshop (Volatile Territories). Its application is flexible and can work at different scales as it is dealing with desirability for adjacency within any framework. Evaluation of its potential and improvements to its performance is ongoing but the conclusions to date are as follows. SASS relies heavily on visual feedback for the evaluation of the 13 External influences for scenario A representing exposure to light. spatial configurations it produces. The dimensional correlation between the RGB color model and the proposed pheromone model allows agents to be rendered on screen according to their respective pheromone targets. As such it becomes evident to the user when the system has settled successfully—when adjacencies have formed between agents of similar color. While the stigmergic communication between agents ensures the model pursues this goal on its own, in more constrained scenarios where space is limited and the agent population is high the system may never reach it. Currently, an element of real-time user interaction has been implemented to overcome this problem. By exposing pa- rameters in the GUI that control the amount of space each agent is trying to occupy, the user can temporarily contract their respective territories giving agents the room required to search for more suitable neighborhoods within the system. The process becomes a supervised form of self-organization where the user is responsible for evaluating and subsequently tuning the system until a satisfactory spatial configuration has been reached. 14 External influences for scenario B representing a privacy gradient. Future development will focus on supplementing this external evaluation mechanism with a more explicit internal approach that better leverages the decentralized nature of the system. By equipping agents with the ability to gauge their “satisfaction” with their current configurations, they could potentially begin to man- age their own parameter sets—allowing them to autonomously tune their behavioral response to their local context. To operate within the model, however, “satisfaction” must take the form of a single numerical value or score that can be calculated by an agent at any point in time. Ideally, the agent’s scope would stay local in doing so in order to avoid excessive computational expense. One approach that capitalizes on the existing pheromone model describes satisfaction as the inverse of the difference between an agent’s pheromone target and the average pheromone concentration of each node in its territory. When adjacent to others with a similar target, an agent faces little resistance in pulling the pheromone concentration of its territory towards its preferred 15 External influences for scenario C representing privacy and circulation

INFORMATION ACADIA 2013 ADAPTIVE ARCHITECTURE 204 16 Play out of stigmergic space application on scenario C 19 An outcome from scenario C

value. This results in a high satisfaction score because, to put it simply, there is little difference between what the agent wants and what it has. When adjacent to others with distant pheromone targets, the opposite is true and a low satisfaction score is returned. By summing the satisfaction score of each agent across the system, an overall fitness value can be produced giving an explicit quantitative basis of comparison between different simulations. Furthermore, by associating this score to the territorial expansion/ contraction of each agent, the effect currently achieved through user interaction could potentially be automated. Hypothetically, the model would be able to both detect unfit equilibrium configurations and maneuver its way out of them resulting in more con- sistent results that are both less reliant on real-time user input and 17 An outcome from scenario A less sensitively dependent on initial conditions.

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Maxwell, I., Pigram. 2010. “Supermanoeuvre - Inorganic Speciation: Matter, Behaviour and Formation in Architecture.” In Contemporary Digital Architecture: Design and Techniques, 209. Barcelona: Links International. ANNALISA MEYBOOM is an Assistant Professor Menges, Achim, and Sean Ahlquist. 2011. Computational Design at the School of Architecture and Landscape Architecture at Thinking. Chichester, West Sussex: Wiley. the University of British Columbia. Her area of expertise is the Menges, Achim; Hensel, Michael, and Michael Weinstock. 2004. integrated design of engineering and architecture. She holds Emergence: Morphogenetic Design Strategies. West Sussex: Wiley. a degree in engineering from the University of Waterloo and Menges, Achim; Hensel, Michael, and Michael Weinstock. 2005. a professional degree in architecture from the University of Techniques and Technologies in Morphogenetic Design. West Sussex: British Columbia. Her research integrates the latest parametric Wiley. methods in architectural design and she has taught studios in Menges, Achim, and Michael Hensel. 2006. Versatility and Vicissitude. responsive environments—integrating performative structures West Sussex: Wiley. into architecture both at the building and infrastructural scale. Moseman, Andrew. 2010. “Brainless Slime Mold Builds a Replica Tokyo Subway.” Discover Magazine. Retrieved 2011-06-22. DAVE REEVES is a computational design researcher and Nakagaki, Toshiyuki; Yamada, Hiroyasu, and Agota Toth. 2010. “Maze- recent graduate at University of British Columbia. He works as Solving By an Amoeboid Organism.” Nature 407:470. a member of the computational design research group at AL_A developing a variety of project specific design digital tools. He Negroponte, Nicholas. 1969. “Toward a Theory of Architecture Machines.” Journal of Architectural Education 23,2:9-12 has taught a number of computational design workshops at institutions such as the Architectural Association in London and Otto, Frei. 2009. Occupying and Connecting: Thoughts on Territories and Spheres of Influence with Particular Reference to Human Settlement. the University of Toronto. Inspired by the likes of slime mold, Stuttgart: Edition Axel Menges. ants, termites and other social organisms, Dave’s ongoing

Reiser, Jesse. 2006. Atlas of Novel Techtonics. New York, N.Y.: research focuses on applications of collective intelligence within Princeton Architectural Press. architectural design discourse.

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