Generative Agent-Based Design Computation
Integrating material formation and construction constraints
Ehsan Baharlou1, Achim Menges2
Institute for Computational Design, University of Stuttgart, Germany http://icd.uni-stuttgart.de
1ehsan.baharlou@icd.uni-stuttgart.de, 2achim.menges@icd.uni-stuttgart.de
Abstract. Agent-based systems have been widely investigated in simulation and modeling. In this paper, it is proposed that agent-based systems can also be developed as generative systems, in which different aspects of performative design can be defined as separate drivers in a proper computational framework. In this manner constrained generating procedures (CGP’s) are studied to integrate the discrete design processes into one system. Subsequently, this generative agent-based design tool is accompanied with generating and constraining mechanism which are informed by material characteristics and fabrication constraints, bringing to the forefront emergent complexity.
Keywords. Computational design; agent-based system; robotic fabrication; constrained generating procedures (CGP’s).
INTRODUCTION
Performative design, as a design process, can be described along with several principles. Integrating such principles into a cumulative system is to in-volve different key aspects of performance in a pro-cess of formation. The integration propro-cess of these aspects requires designing a convenient generative system to explore performative approaches of form generation. In terms of computation, form can be defined as an interaction between internal compo-nents and external forces (Kwinter, 2008). Similar to natural morphogenesis, in computational design modeling the development of form can be informed by the process of materialization, production and construction (Menges, 2008). Each one of these in-ternal components can be described as a separate driver, which in turn, can be synthesized into an in-tegral computational design tool. These integrated drivers interact with each other within an
environ-ment, and exchange that ultimately increase the complexity of the system as a whole.
One procedural approach, is to organize such complexity through a computational framework that incorporates its own elements, rules and inter-actions (Holland, 2000). In some circumstances, this computational framework can exhibit emergent phenomena. In fact, the proper generative compu-tational framework includes both mechanisms to generate possibilities and constraints to limit the range of possibilities (Holland, 2000). Moreover, this computational framework requires to be further specified during the problem solving design pro-cess; developing such computational framework involves three key aspects: generation mechanisms, test mechanisms, and a control strategy (Mitchell, 1990). Furthermore, based on constrained generat-ing procedures (CGP’s), the computational
frame-work should have mechanisms to progressively adapt, or learn, as its components interact (Holland, 2006). A particularly promising method of modeling and simulating such complex adaptive systems (CAS) is agent-based system (Holland, 1995).
RESEARCH OBJECTIVES: GENERATIVE
AGENT-BASED COMPUTATIONAL
DE-SIGN
Recent advances in computational capacity open new perspectives into the implementation of agent-based systems as generative tools within compu-tational design in architecture. The purpose of this paper is to investigate the possibility of integrating generative systems properties and constraint pro-cedures into real-time computational form finding, which are coupled together to exhibit complex emergent behavior.
In this paper the development of this generative system is investigated through constraints generat-ing procedures (CGP’s). This approach gives the pos-sibility to link simultaneously different mechanisms to generate and constraint possibilities, which allow for the exploration of emergent architectural solu-tions. These mechanisms contain discrete design elements and behaviors wherein bottom up meth-odology of behavior-based systems can be useful to organize emergent complexity. This integration is followed by a generative approach of material properties to explore performative formation in ar-chitectural practices, allowing form to emerge from the interaction between agent systems and their surrounding environment. In this investigation form generation is affected by different attributes, which are implemented inside the agents’ data structure.
For this investigation, the agents’ data structure is described by the specific geometrical behavior of bio-inspired plate structures based on the sea urchin. To achieve this, the agents are distributed on the topological space of UV map parameters; the relations between agent-agent and agent-en-vironment are derived from this topological space e.g., it describes the conceptual neighborhoods along with its topological relations. In this context,
the topological space is described by a surface with positive Gaussian curvature and by the fabrication tools, which consist of a KUKA KR 125/2 (6-Axis), and a KUKA KPF1-V500V1 turntable (1-axis). The fabrica-tion configurafabrica-tion also includes a HSD ES 350 spin-dle unit as an effector.
GENERATIVE AGENT-BASED SYSTEM
Agent-based systems as a computational method, facilitates for researchers the study of various fields of science. An agent-based system consists of large number of agents that follows simple local rules and interacts within an environment (Gilbert, 2008). Agent-based modeling consists of defining both the agents and the relationships between them (Bona-beau, 2002); this can collectively exhibit a complex behavior pattern which leads to a global emergent behavior as a result. The individual autonomous agent, as a self-contained learning unit, perceives its environment and takes actions (Mellouli et al., 2004). Accordingly, the agent can learn from its surround-ings by permanently repositioning itself within the overall agent-system and its environment - while ad-hering to a set of flexible behavioral rules. A system of agents thus has the ability to learn and adjust its behavior over time (Figure 1).
In social science, Gilbert (2008) illustrated that agent-based system can be classified into urban models, opinion dynamics, consumer behavior, indus-trial networks, supply chain management, electricity markets, and participative and companion modeling (Gilbert, 2008). On the other hand, Bonabeau (2002) categorized the agent-based system in a business context into flows (evacuation, traffic), markets (stock market, shopbots and software agents), organizations (operational risk and organizational design), and dif-fusion (difdif-fusion of innovation and adaptation dy-namics) (Bonabeau, 2002). These two classifications represent the application of agent-based system for simulation and modeling in any behavioral systems.
In the field of sociology, a generative agent-based approach has been regulated in two steps: Situating agents in a relevant spatial environment and after that utilizing agents’ interaction based on
specific rules to generate another level of bottom-up organized regularities (Epstein, 2006). In this generative method, the systems’ behavior cannot be deduced to behavior of their components, whereby it disregards some of the interactions between the elements (Squazzoni, 2012). Accordingly, the gen-erative method in agent-based system is a bottom-up approach to take advantage of low-level features e.g., material properties, in a manner that enables emergent phenomena.
In relation to architectural design, developing such generative computational framework is easily associated to the different methods for establishing effective organized complexity. One of the features of such adaptation in complex system is emergent properties, which can be obtained through Con-strained Generating Procedures (CGP’s) (Holland, 2000). The advantage of CGP’s in agent-based tem provides the ability to define agents-based sys-tems on mechanisms and constrains - in one specific system. This local generative system as a building block has been implemented in the computational framework as an overall generative system which can be identified as a system property. However, each one of these building blocks or agents has a data structure, in which the mechanisms and con-straints have a great role to find an optimal solution. Accordingly, the definition of mechanisms and constraints are critical in defining real time interac-tions within agent-based systems, whereby this defi-nition must prepare the possibility for a system to become both generative and also have the capacity
to exert the implemented constraints. This real-time interaction is relied on the agents’ data structure; the agents perceive the environment as well the other agents, and based on their defined ontology compute the proper response to any stimuli (Pfeifer and Scheier, 2001). However, the ontology level also depends on the circumstances that will apply to the generative system. This knowledge distribution among agents could be specified locally in order to avoid unnecessary computation.
Consequently, the bottom-up knowledge distri-bution provides agent-based system with behavior-based computation rather than knowledge-behavior-based computation. In behavior-based computation, the topological space is explored with agents along with their specific behaviors to behave in this prob-lem domain, rather than with a specific system that know about the problem domain (Maes, 1993). However based on emergence properties, this tool has difficulty approaching a precise behavior. Therefore, the underlying elements of this tool need enough flexibility to emerge an approximate behav-ior, as a cloud (Miller, 2007).
METHODOLOGY
Agent-Based system: Defining Mechanisms
and procedures
In order to investigate a generative approach for an agent-based system, a CGP framework is developed with both generative mechanisms and constraints. This method maintains a generative computational
Figure 1
A: A Complex Adaptive System similar to that presented by Holland (1995); B: Agent distributions on the topologi-cal space; C: An Agent-Based System, topological interac-tions between agent-agent and agent-environment.
framework to generate all the future possibilities, while maintaining specific constraints or limitations. The mechanisms of these generative agent-based systems are bound to material properties, fabrica-tion and construcfabrica-tion constraints.
Material properties in particular have the ability to characterize geometrical behavior mechanisms. In addition, motion behavior mechanisms have the ability to perform as a sensory motor for each one of these agents, where if the desired conditions are not being met, then the responsible mechanism will re-lease an appropriate response to change the agents’ behavior. This reaction can be differentiated by the agents’ situation, which can be varied from splitting, eliminating, or re-orientation and relocation of the agents’ situation. Predicting a proper mechanism for each situation or problem is not possible in a behavior-based bottom-up system, due to the low-level ontology that is used in it. For this purpose an agent-based system has to deal with only primitive ontology to solve the problems, wherein it has been situated. In the following sections some mecha-nisms related to this generative agent-based com-putational design tool will be investigated.
Motion Behavior Mechanisms: According to Reynolds (1999), the motion behavior mechanisms can be defined in three layers: action selection, steer-ing and locomotion (Reynolds, 1999). These three behavior layers are applicable for a wide range of autonomous motion behaviors, however, it is neces-sary to mention here, that this behavioral hierarchy is not accessible to all range of autonomous agents e.g., it is not appropriate for chatterbot (Reynolds, 1999). In the other hand, the motion behavior mech-anism is specialized in specific behaviors, which is imitated and modeled from certain behaviors of natural entities to relocate autonomous charac-ters. Therefore, this mechanism is suitable only for changing the motion behavior of the system.
In action selection, agents observe the state of their environment and that of the other agents in order to perceive their changes. After this initial perception, agents set appropriate goals, which are proportion to the change of system state and
syn-chronized to the agents’ internal rules; in the steering level, the goal is decomposed into the sub goals that can be represented by the steering behaviors. This in turn can become steering signals, which are intelligi-ble for the locomotion layer; in the locomotion layer, these signals will be converted into motion param-eter of the agent’s locomotion (Reynolds, 1999).
The agent-based system with Motion behavior mechanisms can be influenced by the other steer-ing behaviors, at any moment; this is, to change the agent’s location and orientation. The behaviors, which relate to the agent’s motion, have to be trans-lated to the steering behavior parameters. The steer-ing behavior gives the possibility to accumulate different type of control behavior procedures and based on weight of parameters, they can change the agent’s motion behaviors. Therefore, the locomotion mechanism must be completely independent from steering behaviors (Reynolds, 1999), in which the steering behaviors convert control signals into mo-tion of agents (Figure 2).
Geometrical Behavior Mechanisms: Geometrical behaviors are directly affiliated to material proper-ties which are used in the process of design, fabri-cation and construction. Therefore, the geometrical behavior mechanism is reflection of material proper-ties. In fact, this mechanism defines interaction ef-fects between geometrical characteristic of agents. Since, this investigation is about plate-like structures; therefore this mechanism is limited to the planes geometry. Hence, geometrical interactions between agents are related to geometrical planes intersec-tion; wherein the intersection between a selected agents with surrounding agents, generates a cell with a polygonal structure. The distribution of agents on the topological surface, defines the final shape of agents’ cell. The polygonal shape of this cell (e.g., convex or concave polygon) is closely related to the curvature of the surface (Troche, 2008), which the agents occupy tangentially. Due to the surface syn-clastic definition, the result will be a convex polygon. The geometrical interaction between agents has been related to the tangent plane intersection. However the tangent plane intersection algorithm
(TPI) (Troche, 2008) is not appropriate for defin-ing geometrical behavior mechanisms, due to the knowledge-based structure which has been used inside the TPI algorithm. Instead of operating locally, the TPI algorithm works globally. Therefore an algo-rithm that is based on a bottom-up approach has been developed in order to calculate the real-time intersection between the plate-like structures of the agents’ geometry.
Accordingly, the intersection mechanism has been developed to find the intersection vertices of a generating agent with other neighboring agents (Figure 3); these vertices lay on the tangent plane, which is approximately located on the surface. Fur-thermore, if the agent cell edges (with adjacent agents) are naked and not connected to them, then it indicates that the agent cell relations are interrupt-ed with self-intersection or interpenetration of other
agents. In that case, the generating agent need to send a steering signal to change its state in relation to the neighboring agents and environment.
Agent-Based system: Defining Constraints
As it is mentioned in CGP’S, the generative mecha-nism is coupled with constraints. In terms of archi-tectural design, constraints can be associated with geometrical and fabrication requirements, which lead the generated outputs from interactions be-tween mechanisms toward desired possibilities. It is critical to find a method to relate these intercon-nected design parameters as a part of the genera-tive tool. In term of mathematical biology, the con-straints can be described by morphological spaces, or morphospaces as mathematical spaces (Mitteroe-cker and Huttegger, 2009).
Figure 2
Motion Behavior mechanisms (the attraction and repulsion steering behaviors).
Figure 3
left: The generate agents’ cell right: The intersection mecha-nism by slicing algorithm.
The term morphospace, describes the mor-phological features of generative variations within a solution space or landscape of possible outcomes (Menges and Schwinn, 2012). In this generative tool, the constraints are considered from the geometric limitations and possibilities of the material charac-teristics. In the fabrication phase, these constraints can be described by the morphospace of the fabrica-tion tool. In general, the agents’ geometric attributes dictate the need for various procedures to utilize the generative interaction among the agents. However, the morphospace’s definition overlaps with the dif-ferentiation between the geometrical possibilities and also being producible by the fabrication tool (Menges, 2013). Therefore, the constraints of this investigation are derived from the morphological space, which is categorized in geometrical, fabrica-tion and construcfabrica-tion constraints.
Geometrical Constraints: Since this genera-tive tool is designed for plate-like structures, its geometrical parameters are applicable to the most probable range of plate structures. According to Menges (2013), the plate morphology is identified in three major features (Figure 4): 1) the polygon ra-dius, which is defined as the area of the plate that is calculated based on the polygon vertices, and the perimeter circle which is bounding these vertices; 2) the connection angle, is defined as the angle be-tween connected plates, which is calculated based on the angle between the normal of each connect-ed plates; 3)the polygon angle, which is definconnect-ed as the angle between the polygon edges and is related to the shape of the polygon (in the polygon convex segment(0° to 180°) and in the polygon concave segment(-180°,0°)) (Menges, 2013).
Fabrication and construction constraints: The morphospace of the fabrication tool, in relation to morphological geometry, represents the producible parameters of fabrication. As Menges (2013) men-tioned, with the fabrication tools for this investiga-tion, the morphospace region determines the pro-ducible of geometrical parameters: 1) the polygon radius depends on the distance between the robot and the turntable; 2) the connection angle is limited
by the specification of the effector and the length of the tool; 3) the polygon angle is indirectly influenced by the fabrication tool, in which the constraints are related to the depth of joints who, in itself is deter-mined by the connection angle (Menges, 2013).
RESULT: COMPUTATIONAL DESIGNING
TOOL
Agent-Based system: Agent-based
Pro-gramming
A generative agent-based computational frame-work is established by identifying the agent types along with their attributes (Macel and North, 2009). This identification will be followed by defining the boundaries within the surrounding environment that the agent will explore as a topological and morphological solution space. After the agents and environment are defined, this framework will simul-taneously compute all parallel interactions between agent-agent and agent-environment. These parallel interactions will be associated by sending and receiv-ing through a feedback loop (Holland, 2006). Accord-ingly, in the complex system behavior, convergence to the desired performance criteria is dependent on the positive and negative feedback loops.
This generative agent-based tool is initialized with agents (plate-like structures) and specific en-vironment (synclastic surface). After initiation, the motion behavior mechanism is added to identify
Figure 4
The geometrical and fabrica-tion constraints: polygon radius, connection angle, and polygon angle; similar to that presented by Menges (2013).
the attraction and repulsion between agent-agent and agent-environment. Additionally, this mecha-nism is coupled to other control mechamecha-nisms for the rotation and repositioning of agents. However, after the agent distributions on the surface and the process of finding geometry interaction between agents takes place (generating cell for each one of agent), attraction and repulsion algorithms define coherency between the agents’ cell and its environ-ment. This coherency is defined by cell adhesion and surface edges adhesion. By increasing the value of the cell adhesion, agents begin to present flocking behavior and by decreasing it, agents start to avoid each other within the bounded surface. It should be noted that the agent-to-agent interaction is ex-pressed between one agent and its closest neighbor or one agent and a range of its closest neighbors, in which each one of these can represent different behaviors. In the edge adhesion, by increasing the adhesion value, agents will be attracted to the edg-es and by decreasing it they will gather in a central position - away from all edges (Figure 5).
Consequently, in geometrical behavior mecha-nism, it would be necessary to avoid inappropriate
intersections between connected cells. This prob-lem occurs when the edge intersection lies outside the overlapping boundary area between the two cells. In this case, an algorithm controls that the right intersection between cells exists, it does this by ro-tating the cell or by relocating it on parallel to its normal (Zimmer et al., 2013); through this process the intersection point will gradually change its loca-tion until it fits inside the defined area (Figure 6).
The main functional component of any gen-erative system is it capacity to constrain the pos-sibilities, which are emerged from the generation mechanism. According to the defined constraints for this investigation, the generated cells need to be limited by two aspects: size of the agent’s cell or polygon radius, and the angle between agents’ cell or connection angle. The cell size can be deduced by a regular polygon area formula for convex polygons, after which the radius polygon can be obtained; this radius will be stored in the agents’ data structure to be accessible by the agents during the computation. However, the polygon radius must be in the specific range imposed by the fabrication constraints, in or-der to change the size of the cells, cell division and
Figure 5
left: Edge adhesion (the attrac-tion to the edges); right: Cell adhesion (the attraction and repulsion between agents).
Figure 6
left: Rotating the cell to find the right intersection; right: Relocating the cell on parallel to its normal.
cell growth mechanisms are developed to maintain this size. Additionally, if the polygon radius becomes lower than a predefined size due to the fabrication constraints (as a result of the robotic morphospace) an elimination mechanism will remove the cell from the investigation (Figure 7).
The connection angle is obtained through the normal of the connected cells. Through this arrange-ment, the angle controller mechanism finds the an-gle and checks it for conformance. If it is necessary, the controller generates a steering signal to rotate the cells- this is executed recursively until it reaches the range required for connection angle. This mecha-nism is developed to calculate the steering signal for a generating cell and all its adjacent cells (Figure 8).
DISCUSSION AND CONCLUSION
Current research has proven effective in implement-ing such workflow in the presented case, where robotic fabrication principles of the plate structure morphology have been transferred into the agent’s attributes. For this transfer to be effective, it is nec-essary to precisely investigate and analyze the bio-logical plate structure. Although in modeling of the complex system it is not possible to reach a perfect abstraction; it is possible to find the general behav-ior of the plate structure. This behavbehav-ior will form the basis of the bottom-up mechanism. This bottom up approach, provides the generative system with the possibility to exhibit emergent plate-like structure arrangements and patterns. For example, a
prelimi-Figure 7
Finding the right polygon ra-dius through the cell division and cell growth mechanism.
Figure 8
controlling the connection angle by generating a steering signal to rotate the cell.
narily result of such generative system presented joint conditions that were similar to the Sea urchin, where three plates meet each other at one corner point – rather than four. However, although this be-havior was anticipated, it is also discernible that the lack of construction mechanisms (which naturally has been used in the plate structure), along with in-sufficient construction constraints caused the initial result to be far from what was expected. The initial results might be enhanced by further analysis of biologic model, the fabrication space and the agents emergent behavior so that additional mechanisms and constraints can be subsequently implemented into the tool.
It is also possible to speculate that the results are indicative of the specific means in which agent based tools process the input data. Unlike “Motion behavior” (Reynolds, 1999), the generative agent-based deals with the implementation of material characteristics, geometrical behavior and construc-tion constraints; this implementaconstruc-tion affects agents’ behavior locally and globally. In this manner, agents become a complex adaptive system of systems. It is speculated in this paper that although the pre-sented case studies in the generative agent-based
tool can be accommodated within computational design-, it is imperative to differentiate the aspect of the generative agent-based computation that con-tribute to integrate material system as mechanisms with robotic fabrication constraints (Figure 9).
Some of the consequences of this implementa-tion might steer in a different direcimplementa-tion expanding further our understanding into the Morphospace of robotic fabrication (Menges, 2013). For example, angle and plate control mechanisms empower the design construct in a way that facilitates access for the designer to methodologies that allow him to achieve an optimized plate formation; they also re-duce the need to recourse to design process during construction phase.
REFERENCES
Bonabeau, E 2002, ‘Agent-based modeling: Methods and techniques for simulating human systems’, Proceedings
of the National Academy of Sciences, vol. 99, no. 90003,
pp. 7280–7287.
Epstein, JM 2006, Generative social science: Studies in
agent-based computational modeling, Princeton University
Press, Princeton.
Gilbert, GN 2008, Agent-based models, SAGE Publications,
Figure 9
the final result of the genera-tive agent-based computation tool.
Los Angeles.
Holland, JH 1995, Hidden order: How adaptation builds
com-plexity, Addison-Wesley, Reading, Mass.
Holland, JH 2000, Emergence: From chaos to order, Perseus Books, Cambridge, Mass.
Holland, JH 2006, ‘Studying Complex Adaptive Systems’,
Journal of Systems Science and Complexity, vol. 19, no.
1, pp. 1–8.
Kwinter, S 2008, ‘Who’s Afraid of Formalism’ in C Davidson (ed), Far from equilibrium: Essays on technology and
de-sign culture, Actar, New York, pp. 144-149.
Macal, C and North, M 2009, ‘Agent-based modeling and simulation’, Proceedings of the 2009 Winter Simulation Conference (WSC), pp. 86–98.
Maes, P 1993, ‘Behavior-Based Artificial Intelligence’ in J Meyer, SW Wilson and HL Roiblat (eds), From Animals to
Animats 2. Proceedings of the Second International Con-ference on Simulation of Adaptive Behavior, MIT Press,
Cambridge, Mass. [u.a.], pp. 2–10.
Menges, A 2008, ‘Integral Formation and Materializa-tion: Computational Form and Material Gestalt’ in B. Kolarevic and K. Klinger, (eds), Manufacturing Material
Effects: Rethinking Design and Making in Architecture,
Routledge, New York, pp. 195-210.
Menges, A 2013, ‘Morphospaces of Robotic Fabrication. From Theoretical Morphology to Design Computation and Digital Fabrication in Architecture’ in S Brell-Cok-can and J Braumann (eds), Rob/Arch 2012. Robotic
fab-rication in architecture, art and design, Springer, Wien,
pp. 28–47.
Menges, A and Schwinn, T 2012, ‘Manufacturing Reciproci-ties’, Architectural Design, vol. 82, no. 2, pp. 118–125. Mellouli, S, Moulin, B and Mineau, G 2004, ‘Laying Down the
Foundations of an Agent Modelling Methodology for Fault-Tolerant Multi-agent Systems’ in D Hutchison, T Kanade, J Kittler, JM Kleinberg, F Mattern, JC Mitchell, M Naor, O Nierstrasz, C Pandu Rangan, B Steffen, M Sudan, D Terzopoulos, D Tygar, MY Vardi, G Weikum, A Omicini, P Petta and J Pitt (eds), Engineering Societies in
the Agents World IV, Springer Berlin Heidelberg, Berlin,
Heidelberg, pp. 275–293.
Miller, JH and Page, SE op 2007, Complex adaptive systems.
An introduction to computational models of social life,
Princeton University Press, Princeton, New Jersey. Mitchell, WJ, 1990. The Logic of Architecture: Design,
Compu-tation, and Cognition, Cambridge, Mass: MIT Press.
Mitteroecker, P and Huttegger, SM 2009, ‘The Concept of Morphospaces in Evolutionary and Developmental Bi-ology: Mathematics and Metaphors’, Biological Theory, vol. 4, no. 1, pp. 54–67.
Pfeifer, R and Scheier, C 2001, Understanding intelligence, MIT Press, Cambridge, Mass.
Reynolds, CW 1999, ‘Steering behaviors for autonomous characters’, Game Developers Conference, vol. 13, pp. 763–782.
Squazzoni, F 2012, Agent-based computational sociology, Wiley and Sons, Hoboken, N.J.
Troche, C 2008, ‘Planar hexagonal meshes by tangent plane intersection’, Advances in Architectural Geometry, Vien-na, pp. 57–60.
Zimmer, H, Campen, M, Herkrath, R and Kobbelt, L 2013, ‘Variational Tangent Plane Intersection for Planar Po-lygonal Meshing’ in Advances in Architectural Geom-etry 2012, eds L Hesselgren, S Sharma, J Wallner, N Baldassini, P Bompas and J Raynaud, Springer Vienna, Vienna, pp. 319–332.
