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7.2.3 Hybrid personalised function training
In Chapter 5, we discussed several means for eliciting data with which to train the function ap-proximation. In general, having each user fully train a personalised approximation may be too cumbersome, yet having a single expert or a synthesised opinion form the basis for the training might not suit every individual user.
Although it might be assumed that different users have different perceptions, our preliminary in-vestigations1 indicate that people in general have some fairly similar perceptions. This is an area that has been well researched in psychology, and in particular language has been shown to have a great impact on shaping people’s perceptions. Davidoff et al. [1999], for example, demonstrates that memory and perceived similarity of colours are predicted by the colour terms in a speaker’s language.
As such, one could imagine a system in which an expert or synthesised opinion forms the basis for the training of the function approximation, subject to some small subset of per-user training to personalise the approximation. One easy way in which this could be accomplished using the framework of this thesis was described in Section 5.6.1, in which it was suggested that certainty values could be used for exactly this purpose. Using such an approach has the benefit of maintaining both the common and individualised data, and seeking a best fit where the user data has a slightly greater weighting.
7.2.4 Direct comparison to other state of the art interfaces
The experiments presented in this thesis were aimed at showing that an adjectival interface was not only usable, but also provided an advantage over directly controlling procedural parameters. Whilst a more direct comparison to other state of the art interfaces could have been performed, this would have restricted the problem domain being addressed — for example, much work has been done in producing visual interfaces for computer graphics content, but such interfaces are not useful when generating procedural speech.
Now that an adjectival interface has been shown to have promise, it would be interesting to conduct more direct comparisons with state of the art interfaces in specific problem domains. One could even imagine a hybrid approach — for example, an adjectival interface could be used to select a number of virtual environments, which can then be explored using a design gallery [Marks et al., 1997].
7.2.5 Online training
Closely related to the previous point is the topic of online training. Here, the user could train the system as they use it — if, after generating content, they felt that the content did not match the
1See Appendices A and B
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descriptors that they had used, they would then have the opportunity to specify how the content should be described and, in so doing, further train the system. Similarly, if the user was satisfied with the content generated, then this information could be used to strengthen some of the underlying approximation controls.
7.2.6 Local control
The technique proposed in this thesis offers only global control of content generation — the adjectival descriptors apply to the content as a whole, and it is not possible to differentiate the behaviour on a finer scale. For example, consider the task of landscape generation presented to the users in Chapter 6: although the landscape might have a suitable overall look and feel, it would be extremely useful to be able to specify individual characteristics for particular regions of the landscape.
This is not an easy problem to solve, and could be seen as being very specific to each particular procedural domain. One avenue of exploration would be to define different sets of procedural param-eters at different points in space or time — depending on the nature of the underlying procedural system — and then smoothly interpolate between these. An alternative idea would be to first gen-erate content using a global description that captures the overall feel, and then somehow hone in on specific areas to apply local modifications.
7.2.7 Natural language parsing
Whilst the use of adjectival descriptors has proven to be successful, it would be even more intuitive for users if they could describe a scene using natural language. WordsEye [Coyne and Sproat, 2001]
could perhaps be extended to include the notion of a procedural model in its database, thus allowing for the mapping from nouns to particular procedural models. The adjectives used in conjunction with each noun could then be applied to the procedural model using a technique similar to that of this thesis.
7.2.8 Use of non-parametric procedural inputs
In Chapter 2, a wealth of procedural techniques was discussed. Several of these use inputs that are not strictly parametric, such as image maps or sketches. It would be interesting to consider the integration of such techniques into an adjectival framework — perhaps by having the user supply input in some other form and then modify that input based on an adjectival description.
As an example, consider the landscape generation system of Chapter 6. If a user wanted finer control over the heightmap used, it would be a relatively straightforward task to have the procedural system skip its internal heightmap generation and simply use the user-provided heightmap instead. The
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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.