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126 CHAPTER 7. CONCLUSIONS AND FUTURE WORK

adjectival interface could then be employed to perform the remaining tasks, such as landscape shading and the creation of rivers, lakes and trees.

One could also conceive of a procedural system that takes as input a heightmap or a sketch, and warps or modifies that input in some way based on a set of procedural parameters. An adjectival interface could be employed in such a setup to control the modification process, which can then be applied to other alternative forms of input.

7.2.9 Incorporating pre-defined content

Closely related to the previous point, is the notion of incorporating some pre-defined content and having the adjectival interface generate additional, augmenting content. With respect to landscape generation, for example, one could conceive of a user wanting to place several buildings onto the landscape, using existing architechtural models, and having the landscape evolve around the build-ings. This is in some ways an extension from supporting non-parametric procedural inputs, to more generally supporting alternative forms of input that in some way interact with the procedural generation.

Whilst this seems like it would largely require adaptation of the procedural model used — adapting the model to cater for defined content — it does tie into the adjectival realm, in that the pre-defined content might itself confer some adjectival properties. Consider the example of placing a house on a landscape, where the landscape is generated using an adjectival interface. If the user wished for a “flooded” landscape, care would need to be taken to ensure that the house nonetheless is placed on a piece of solid land. A wooden house in the middle of a desert also seems improbable;

however if that house were situated next to an oasis, that might be more plausible.

In general, therefore, one could imagine some sort of feedback mechanism whereby the pre-defined content is able to affect the adjectival description of its locality — something akin to open L-systems [M˘ech and Prusinkiewicz, 1996] which allow for communication between the procedural model and its environment.

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Appendix A

First investigation: adjective

representation and consistency of thought

One of the key characteristics of a descriptive method for the generation of procedural content is how the adjectives are represented. Of additional importance to the complexity of the final system is how similar people’s perceptions are, as high similarity could lead to a simplified mathematical model. The first user investigation aimed at addressing these two issues.

A.1 Outline of investigation

A simple procedural environment was defined with 16 procedural parameters, which are shown in Table 16. For simplicity, the environment was constrained to be an island in the middle of the sea, with a limited selection of tree types and no additional foliage. With this investigation being intended as very prototypical and solely for the purpose of gathering some more general information, the focus was not on high levels of realism. This also allowed for a real-time rendering, giving users the opportunity to explore the environment at their will instead of being constrained to discrete images. Some examples of the types of environments generated are shown in Figure 52.

With a system in place for generating environments procedurally, 15 environments were chosen as exemplars of the diversity achievable by the system. The values of the procedural parameters used for these 15 environments are shown in Table 17.

15 test subjects were solicited and given the opportunity to explore the 15 environments, and describe them. Before interactively exploring and describing any of the environments, each user was first

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APPENDIX A. FIRST INVESTIGATION: ADJECTIVE REPRESENTATION AND CONSISTENCY OF THOUGHT

(a)

(b)

Figure 52: Some procedural environments generated for our first investigation.

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A.1. OUTLINE OF INVESTIGATION 129

Parameter Description Min Max Default

time-of-day The hour part of the time of day 0 23 12

perlin-increment Inverse frequency on Perlin noise used to cre-ate height values for the terrain.

0.0001 1.0 0.01 perlin-max Maximum height value of Perlin noise. 0.0 300.0 150

min-gaussian Minimum Gaussian output. 0.0 1.0 0.0

max-gaussian Maximum Gaussian output. 0.0 1.0 1.0

num-gaussian Number of Gaussians to blend with Perlin noise in creating terrain.

5 60 20

min-radius Minimum radius of Gaussian landscape com-ponents.

25 225 25

max-radius Maximum radius of Gaussian landscape com-ponents.

25 225 225

min-rainfall Minimum amount of rainfall that falls on the terrain.

0.0 1.0 0.0

max-rainfall Maximum amount of rainfall that falls on the terrain.

0.0 1.0 1.0

rainfall-variance Inverse frequency of rainfall variance over the terrain.

0.0 1.0 0.25

palm-density Proportion of landscape locations that should have palm trees.

0.0 1.0 0.0005

cactus-density Proportion of landscape locations that should have cactii.

0.0 1.0 0.0005

fir-density Proportion of landscape locations that should have fir trees.

0.0 1.0 0.0005

cloudiness Proportion of the sky that is covered by cloud. 0.0 1.0 0.5 rain-threshold Minimum amount of cloud-cover required to

generate rain.

0.0 1.0 0.7

Table 16: Procedural parameters for our first investigation.

shown a set of images captured as single-frame screenshots of the environments — the purpose being to make the user aware of the diversity in the system, serving as a guide in their descriptions.

After interactively exploring an environment, the user was asked to describe their environment using a slider interface, as shown in Figure 53. Seven adjectival descriptors were provided, which represented either a single adjective or an antonym-pair of adjectives (for example “wet/dry”).

These descriptors were chosen to reflect the visual and affective differences in the environments shown. Each descriptor had an associated slider control to indicate its relevance — as the slider was adjusted, the label associated with the slider would be updated to reflect both a numerical value and a categorical description corresponding to the slider position. The categories were defined by a simple partitioning of the numerical range into equal-sized partitions, with 5 categories per adjective (giving 10 categories for antonym-pair descriptors). User comments on individual environments were also collected, as well as general comments once all the environments had been viewed and described.

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130 APPENDIXA.FIRSTINVESTIGATION:ADJECTIVEREPRESENTATIONANDCONSISTENCYOFTHOUGHT

Environment #

Parameter 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

time-of-day 12 14 16 19 5 7 7 11 13 17 10 18 0 18 8

perlin-increment 0.01 5e⁻³ 5e⁻³ 0.01 0.01 0.01 0.01 0.01 0.01 5e⁻³ 2.5e⁻³ 0.02 1e⁻³ 0.01 0.01

perlin-max 150 50 30 150 150 200 200 75 300 200 100 100 100 300 50

min-gaussian 0 0 0.7 1 0 0 0 0 1 0.6 0.6 0.6 0 0.6 0.6

max-gaussian 1 0.6 1 1 1 1 1 0.6 1 1 1 1 1 1 1

num-gaussian 20 20 40 30 30 30 30 40 40 40 40 20 20 40 15

min-rainfall 0.5 0.6 0.6 0 0 0.8 0 0 0.8 0.8 0 0.6 0 0.8 0

max-rainfall 1 1 1 1 1 1 0.3 0.3 1 1 1 1 1 1 0.3

rainfall-variance 0.25 0.25 0.25 0.5 0.1 0.1 0.1 0.1 1 1 1 0.25 0.5 1 0.25

palm-density 3e⁻³ 1.5e⁻⁴ 1.5e⁻³ 1.5e⁻³ 1.5e⁻³ 1.5e⁻³ 1.5e⁻³ 1.5e⁻³ 0 4e⁻⁴ 4e⁻⁴ 5e⁻⁴ 1.5e⁻³ 4e⁻⁴ 3e⁻³

cactus-density 5e⁻⁴ 0 0 0 0 0 1e⁻⁴ 1e⁻⁴ 0 0 1e⁻⁴ 0 0 0 0

fir-density 7.5e⁻⁴ 0 0 1e⁻⁴ 1e⁻⁴ 1e⁻⁴ 1e⁻⁴ 1e⁻⁴ 0 1e⁻⁵ 1e⁻⁴ 5e⁻⁴ 1e⁻⁴ 1e⁻⁴ 0

cloudiness 0.5 0.1 1 0 0.8 0.8 0 0 0.9 0.2 0.2 0.9 0 0.9 0

rain-threshold 0.7 0.7 1 1 0.8 0.8 1 1 0.7 0.3 0.3 0.7 1 0 0.7

min-radius 25 25 25 100 100 100 100 25 100 25 25 25 100 25 25

max-radius 225 40 30 100 100 125 125 30 200 200 70 75 150 200 225

Table 17: The parameter values used to generate environments for our first investigation.

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Figure 53: The user interface for capturing descriptions in our first investigation.

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