Navigating Design Space by sivam krish

NAVIGATING DESIGN SPACE Design as search

Why design is search In computational design ‐ design is search and nothing else. It is unfortunate that to get engineers to work, you need to give them a “problem” ‐ else they can’t get started, even with extra cups of coffee. Design in engineering is considered a “problem solving activity”. If there is no “problem” then there is no activity. Designers on the other hand, belong to a different species. Many of them, especially the good ones, have a very different obsession – of the “search for meaning” – that relate to existential issues of the human kind. In computational lingo – we may state that their “search space” includes greater issues that are un‐computable and relate to the human condition. But let us for now accept the simplistic model of design as a search – for a problem that we can define. To commence search, we need 1. A mature representation of design 2. A representation of design space (mostly parametric) 3. A desirable answer (stated in terms of performance) Design here is setup as a solvable problem – like in exam papers. We already know the answer and we know the marking scheme. We not only get to set the paper, sit for the exam and also mark it ourselves. But in this happy scheme there is a big problem. The search space is huge. To give you an idea, let us take the example of the amount of data that is in your own DNA. If you stretch the entire DNA in your body, it would cover about 70 times the distance between earth and the sun. Now that is a lot data. Design space is about representing design data. Even for relatively simple designs, the corresponding design space can be very, very large. Design is about navigating this very large space in search of an appropriate solution. But there is a solution. We put on our blinkers and focus on a solution that we know exists.. We head straight towards it – efficiently, without looking here and there. In other words, we drastically reduce the search space – to a navigatable size. Such activity is called routine design activity. Creative design exploration is different. You need to put on your space suite. The design space is far too vast that procedures are of little use. To navigate this universe, you need to bring in the most complex machine in the universe into action – your brain.

Design development The “search “in designs space begins when the design is still being conceived. At this stage, the details are sketchy; what that means is that the design can develop in so many different directions. So with un‐ certainty come uncharted possibilities. Hence, sketch schemes represent much larger design spaces than finalized schemes – which are represented by a point in design space. It is known that design is the process through which uncertainty is reduced. Early stage design not only involves large amounts of uncertainty but is explored with multiple design representations. The combination of the two allows designers to explore very large designs spaces – with somewhat low Final Design levels of certainty. It helps the designer to identify promising regions of designs space in which to invest further design development effort.

Multiple design options

You can see that early stage design (indicated in gray) represent larger design spaces and late stage design (indicated with darker spots) represent smaller more resolved designs. Early Stage – large number of possibilites

Final Stage > Single representation

High levels of uncertainity

High levels of certainty

Navigating the developmental space We need to visualize not only the final design space but the design developmental space – well before it gets to the parameterizable stage, as design development is a journey through this space. While it is easy to represent late stage design in computational form, it is difficult to do the same for early stage designs. Design development entails the creation and rejection of a series of conceptual models that cannot be coded. Not that we cannot do it. We just do not know how to. But this is not the case in biology. The entire developmental process is code (genes) driven. Not that it does not create and discard designs. It does. Worse still, it does it live. We and all life forms that came before us are part of its design developmental processes. We will look at it in detail later. In the human designs process, the early stage is brain driven, while we may not be able to map concepts accurately in design space, we need to understand how it works. Let us explore the different ways in which designers search the vastness of design space. Manual Design Exploration

Manual Design Exploration with parametric optimisation

Generative Design Exploration with genetic models

Search Space

In the manual design processes, the designer explores various instances of designs within the design space – initially, at sketch level but during design development, the design will converge to a particular region and will be finalized.

Search Space

The main difference here is that closer to finalisation, the designer or engineer tweaks a parametric model to find the “optimum” design, considering various performative factors. Design optimisation happens here within a small region.

Search Space

In a generative design process, design exploration starts at the early stage of the design and continues through to the final stage. Genetic models also cover much larger regions of design space. Overall, a much larger area of design space is explored.

The purpose of developing computational approaches is to empower the designer to explore larger design spaces. We are assuming here, that design requires a human designer. This is mostly true, when it is difficult to explore design spaces automatically and when it is not possible to numerically define the desired target state. If you can define the desired target state ‐ and the design space to be explored is limited to a defined parametric design space ‐ then one of the numerous optimisation methods will be 4

capable of finding the “optimal design”. Optimisation methods were designed primarily for such purposes. The difference between parametric modelling and genetic modelling Parametric models are generally created to explore parametric variations of a design representation by varying the driving dimensions of the design. A genetic model is a more sophisticated version of the same design representation but is capable of exploring a much larger region of the design space. The ability to explore larger design spaces comes from the way the geometry is constructed – layer by layer, in order to facilitate geometric expressions at each stage of the build processes. It would have the ability to create a ‘rough’ or sketch representation or a more detailed representation. It would also have the ability to switch regions of design on and off for the purposes of exploring variations on particular aspects of the design. Also they are created to fit into a larger generative scheme. For example, the generative design of windows needs to work within the generative schemes for wall designs; in that, the genetic models should be constructed with an awareness of the overall developmental process of a design. They also need to help the designer navigate the search space by making it possible to categorise the expressions of the models into recognizable species of design. In short, they have to be structured to facilitate the human‐driven generative design processes. The nature of complex performance spaces Design parameters vary as they are simply parameters defined by designers to drive the design. But the resulting design may change erratically and disproportionally in some cases. If the performance parameters changed gradually, the performance space would be made of gentle slopes and if it changed erratically, it would be made up of sharp peaks and valleys. Small changes in design parameters could result in sharp changes in performance, making it difficult to predict the behaviour of design changes. The design of gemstones provides a good example. Gemstones are complex optical devices that are cut to exacting geometries, to create complex optical effects: namely brilliance, fire and scintillation. Brilliance signifies the amount of light reflected back by the gem; fire signifies the splitting of the red and blue ends of the visible spectrum and scintillation, the sparkling effect as you move the stone encased by the combination of return of light through the face of the stone and the leakage of light through certain facets of the back of the stone. These three important design parameters are the crown angle, pavilion angle and the table size as a percentage of the diameter. All the optical parameters are altered significantly by small changes in design parameters. In other words, the performance space is not smooth and predictable. Shown is the cross section of one factor – brilliance. The other factors are equally complex. The evaluation of the “ideal cut”, requires the weighted combination of all these, mountains full of ravines and valleys; only experts know their way around. Tolkowsky was one such expert who discovered the highest point in this mountain range – hence the Tolkowsky cut. Then many mountain peaks were discovered. But it is important to remember that when a mountain is made by three independent parameters of brilliance, fire and scintillation (only recently quantified) different folks see different mountains – experts have a tendency to

disagree – not because they wish to, but because they evaluate the importance of various factors differently. Creative design is in such a turf.

Brilliance – the amount of reflected light, plotted against crown, pavilion and table size.

From this example, we hope it is clear how the complexity of the performance space and the variations in judgment criteria make some design problems more complex than others.

There are two types of performance spaces. One has multiple sharp peaks and valleys and the other is smooth and a lot more predictable. One is difficult and even dangerous to navigate. If you move a little in the wrong direction you are in for a big fall in rough terrain, while it is not the case if the performance changes gradually. One requires an expert with the knowledge of the terrain to explore. The other does not require high levels of expertise to navigate. Great deigns happens at the peaks and not everyone knows how to get there. The performance space of diamonds is like the rugged mountain – where there are many peaks which were discovered by “experts”. Now you can add a level of complexity to this – the mountains look different to different people base on their own definition of the performance factors and the relative importance they give it. Now in this context, the engineering view of “optimising design” makes little sense. It makes a lot of sense when everybody agrees on the mountains and the relative importance of the various parameters. Many engineering “problems” fall nicely into this category. Though for centuries, diamonds represented the 6

deep mysteries of the universe, symbolizing for many complex feelings such as love – it is also a commodity that is traded. Hence, finally, the experts came together to agree on how they were going to measure its value by using various measurable performance factors. The point here is that, it is possible only through the agreement of a large number of people agreeing and willing to attribute $ value to important parameters. Imagine a similar scenario happening in design. The hallmark of a creative design problem is certainly the difficulties in mapping design to performance spaces, uncertainty over both their definition and relative importance of various performance factors. These make creative design problems unsuitable for computational optimisation. It takes mountaineers with many years of experience and many falls to be able to climb treacherous uncharted mountains. It is the same with creative design. It requires designers – trained and experienced in design. If you give this task to engineers they will ask you first for a map, except design engineers; because they create maps as they climb. Design for them is a discovery. Differences between automated and human‐driven design processes We are now beginning to see an influx of engineering design process in architectural design, as the boundaries that once divided architects and engineers begin to fade. While they both have their own philosophies on design, computational approaches to design is now bringing them closer, mainly because they are able to share and work on common computer based representations of design. The development of creative design processes is about developing the skill to explore large search spaces. Architects are good at this. They are trained for this. Engineers are generally trained to operate within tiny design spaces. In many architectural practices, they are employed primarily to optimise, which happens mostly in later stages. However, in more enlightened practices, engineers are involved much earlier in the design process, to help trim the search space so that architects can explore within viable regions of the design space – without having to change the design, later on to make it more efficient. Viable region Best design Viable Region

Performance Space

Given the vastness of design spaces, designers wish to explore only viable design spaces. This is much easier in the latter stages of the design and more difficult in early stages where the design representation is not fully formed. In the engineering speak, “specifications” are first set to communicate this expectation. This could be a target or a pass/ fail criteria. Very often, it will contain a number of different important aspects of the design to be used to determine if the designs passes or fails. We can use this to determine the viable region if we know how the performance space maps to design space.

If you have a mature design representation, it would be possible now to compute most of the performative aspects. But this is difficult to do so in early stage design, as there is insufficient design data required for analysis. On the other hand, we can claim that we can do it with low levels of certainty. You would have heard designers looking at their sketches, scratching their heads and saying that this “may” work – indicating the levels of certainty. But many, especially the more famous designers, will show you a sketch (after the project is complete) that almost looks like the final design and claim that their brilliance got them to get it right in the first attempt. But practicing designers know that design development is a treacherous process and one that is filled with many disappointments and dead ends – requiring many re‐ starts. This makes developing genetic models difficult ‐ because they too require investment of time and energy. Nature gets clever here; it saves the clever stuff in our genes – so that they can be re‐used. But, in our design processes we find it difficult to create multiple design models or even a single computational model. So we resort to sketching. By sketching we create in our minds different design representations – for the purposes of exploring the performance spaces in our minds. Another way to look at the viability of designs is to set a “Fitness value” (eg. zero for fail and 10 for a good design). Now, if we can also make this fitness value a binary – 0 or 1 we can make the designs “Pass” or “Fail”. One approach then is to search for the design with good marks, the other approach is to search for the good designs by discarding bad ones. Typically we do both. We fail students and prevent them from progressing to the more advanced classes and we reward the top performers with prizes. We are used to this dual approach.

The Ying and Yang of computational design The harmonious co‐existence of the opposites is a great concept that we can use to understand dual concepts. The opposites here are the “pass” and the “fail”. Some design processes focus on the pass and some on the fail: In reality we use both.

One extreme case of pass approach is the numerical specification of “goodness”. Our school teachers took this approach as they marked our exam papers; like 60% for the right answers, 25% for the style of expression, 10% for hand writing, and 5% for spelling. While marking schemes have some merit in evaluating a student’s knowledge in the sciences, they prove to be problematic when it comes to humanities where the issues are complex – the teacher’s own biased judgments come into play. However, without a marking scheme, we will not have a schooling system. Without a marking scheme we will not have efficient bridges or roads; so we do not argue its value; but we must know its limitations. But designers are not like school principals who hand out “best student” prizes. Engineers are more like the school principals, in that they may believe that goodness is measurable and that by combining many such measures (using magic formulas) one may obtain the “best” design. For reasons explained before, the complexity of most design problems make goodness not only incomputable, but also meaningless. Because designers have different measures and assign different values to those measures and so will those who use the designs. Designed objects means different things to different people in different contexts. Not that good designs do not exist, just that they come with a large dose of uncertainty and subjectivity. Another approach is the “fail approach”; here we eliminate only the bad designs and seek the best through a process of elimination. Often “We do not know what is best” but we know what will not work. So we can use this approach to eliminate the regions that are unviable is search space. This is a filtering or an exploration approach where we use various criteria to filter out the unviable regions based on pass‐fail criteria. Here, the designs that pass through the various filters help define the “viable” design space. However, viability cannot be determined accurately in the early stages of design due to the uncertainty involved. In such a context, a pass/fail approach may make more sense. Because in computational design we need to think not in terms of a design instances but in terms of regions or in design space ‐ that are expanded and constricted during design development. The pass/fail approach will serve us well in this. So we have now explained the ying and yang of search. One is a positive approach, where an ideal state is set, and the other is a negative approach, where we cull the unsuitable in our search for the good. 9

How do we survey a trillion hectors of design space? The answerer is that you cannot. A more convenient answer is that you need not bother. The deep blue computer did not have to find out the best chess moves, it had to figure out better moves than the best human chess player, to be declared “grand master”. Design is a human activity where the most limiting constraints are time and energies of the designer. The use of computers can be used to extend the energies and greatly reduce exploration time, but if the burden of choice is placed on the designer – due to the complexity of the designs, then this caps the region that can be explored. The size of the regions explored is depended on the expressive nature of the genetic model (in‐terms of each model’s ability to represent a design space) and the number of genetic models deployed to explore the performance space. We should not assume here that only one genetic model is used. Constructing the genetic model would take up a significant portion of the designer’s time. Because the genetic model is incomplete, the designer’s imagination is used to complete what is missing. An imaginative designer will be able to asses a large range of designs around a design concept even though the design representation is at an early stage. Hence the imaginative capability of the human designer comes into play enlarging the search space based on limited instances of designs in design space. In any search, it is important to search far and wide in the initial stages, identify regions of promise and then spend more time within these regions developing a viable design. We don’t have a formula as to how best to do this. But in terms of assessing designs, we know that it makes sense to only assess designs that are set some distance away from each other in performance space (but this is only true of the performance space is mountainous). Once we identify a promising region in performance space, we can then use a much more fine‐grained search of that region. Hence search strategies have to be developed to conserve the designer’s attention span – which is the most limiting factor in design exploration. We hope that you are beginning to see design as an exploration in search spaces using an evolving set of representations which become more and more complete towards the end of the design process. We also hope that you are able to appreciate that design is about setting up the search space and searching within it for desirable solutions. As this search space is very large, we hope that we do not have to convince you that design is about constricting the search space in to much smaller viable regions out of which you can pick and choose the most desirable design. 10

Optimisation vs Exploration Two different ways of reducing search space is illustrated. One shoots towards a target and the other uses filters to narrow the feasible region. The fundamental difference here is that you need the definition of a desirable end state to run an optimisation processes and you don’t need one to run an exploration process. While both processes are about reducing search space, in optimisation you will end up with a singular solution that is close to the target state. Optimisation can be done in many ways. The optimisation methods that converge to an answer are 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Memetic algorithm Differential evolution Dynamic relaxation Genetic algorithms Hill climbing Nelder‐Mead simplicial heuristic Particle swarm optimization Simulated annealing Tabu search Reactive Search Optimization

Out of this Genetic algorithm is now beginning to be used in combination with CAD systems. Optimisation schemes require numerical goals. In the case of genetic algorithms, fitness values provide this goal. The design space is explored by re‐combing the genetic materials of desirable designs – hence the name genetic algorithms. Populations of designs are maintained and the more desirable offerings are bred and re‐bred until they reach the target state. The process of elimination happens here based on the overall goal or fitness value and the path of exploration (the constriction and expansion of the solutions space) is handled by the genetic algorithm. In exploration based methods, there is no need to set a target state (or fitness value). But filters are used to eliminate unviable designs based various performance criteria– so that the designer’s selective energies can be focused on smaller and smaller regions of the design space. The process of elimination happens here based on each performance criteria (in contrast to the combined goal criteria in genetic algorithms). The path of that exploration is also left to the designer. This path is bound to zig zag through design space, leaving a train of abandoned schemes – but converging a larger search space. In contrast the optimisation approach is usually based on a single model covering much more very limited design space. The exploration approach is suitable for situations where it is not possible to define a desirable end state before hand and it is assumed that designers will be able to recognize a good design when they see it. In a way, it is a designer driven exploration process that is computationally supported.

The suitability of these two different approaches depends on the nature of the design problem. We need a better understanding of what happens during design processes to help us select the most appropriate way of navigating design space.