Enhancing Objet Trouve Methods in Graphic Design (A Feasibility Study)

Adobe Photoshop等软件产品在过去十年中彻底改变了平面设计，使设计师更具创造力，更有效率。每当设计师使用这样的软件时-无论是为了寻找合适的图像这样简单的事情，还是更复杂的事情，比如制作一件艺术品或将整个设计放在一起-他们都会对他们所呈现的图像/艺术品/设计（我们称之为人工制品）进行美学判断。他们会选择一个图像，但拒绝另一个;他们会玩弄图像处理工具，拒绝许多可能性，并开发其他的，直到他们对最终结果感到满意;他们会尝试一个设计，然后放弃它而支持另一个。理论上，设计师所做的美学选择可以通过他们使用的软件进行分析，这些信息可以用来使软件更好。这种改进可以是更高的效率，这样设计师就可以更快地完成他们的项目，或者它可以是改进的创造力，这样设计师就可以在他们可用的时间内尝试更多的可能性。虽然在这个领域已经有了一些研究，但一般来说，图形软件并不利用用户的任何美学信息，并且有很大的机会引入人工智能技术来实现这一目的。如果我们坐下来要求设计师描述为什么某些人工制品是好的或坏的，并尝试写下近似他们选择的方法，我们最终会使用数学概念，如选择两个值中最大的，或取平均值，或将两个想法组合在一起，等等。实际上，我们将在数学上模拟他们的审美偏好。本项目的目的是测试使用我们的人力资源自动化数学理论形成软件的建模过程的可行性。人力资源有很大的成功与发明的数学概念，并发现定理的真正利益的数学家在数论，图论和各种代数域。此外，它最近被用来发明数学概念，近似的计算机艺术应用场景的价值。这个小的案例研究突出了在平面设计应用中使用HR的潜在好处。我们提出了一个两阶段的方法来将HR与平面设计软件集成，以便用户使用该软件，忽略HR在后台运行。我们假设用户在开始会话时对他们想要生产什么样的人工制品只有一个模糊的想法，并且很乐意以objet trouve（发现艺术）的方式工作。也就是说，他们会在那些呈现给他们的灵感中寻找人工制品，然后积极地追求他们的任何想法。在第一阶段，我们将用主题化的方法取代通常的临时浏览可能性，我们将使用HR来发明评估人工制品的方法（适应度函数），然后向用户呈现在每个主题中得分很高的人工制品。在第二阶段，我们将使用闭环学习来对用户审美的近似进行数学建模。当模型足够好时，它将被用来引导用户更快地找到最终的artefact.We将在三个应用领域测试这种方法，即进化艺术，图像检索和视频游戏的城市设计。我们已经组建了一个在这些领域具有丰富经验的项目合作伙伴团队，我们有软件可用于整合人力资源。我们计划支付受试者进行实验，我们将测试他们是否更有效率和/或更有创造力的增强应用软件。这是一个有风险的项目，但如果成功，它可能会导致设计师在创作过程中利用智能软件的方式发生根本性的变化。

Software products such as Adobe Photoshop have revolutionised graphic design in the past decade, by enabling designers to be more creative and more efficient. Whenever a designer uses such software - whether it is for something simple like finding a suitable image, or something more complex, like making a piece of art or putting together an entire design - they will be making aesthetic judgements about the images/artworks/designs (which we call artefacts) that they are presented with. They will choose one image, but reject another; they will play around with image manipulation tools, rejecting many possibilities and developing others until they are happy with the end result; they will attempt a design then scrap it in favour of another.In theory, the aesthetic choices made by a designer could be analysed by the software they are using, and this information could be used to make the software better. This improvement could be in the form of greater efficiency, so that the designer can expect to finish their project quicker, or it could be in the form of improved creativity, so that the designer can expect to be able to try out more possibilities in the time they have available. While there have been a number of studies in this area, in general, graphics software does not utilise any aesthetic information from the user, and there is a great opportunity to bring in Artificial Intelligence techniques for this purpose.If we were to sit down and ask a designer to describe why certain artefacts are good or bad, and try and write down a method for approximating their choices, we would end up using mathematical concepts such as choosing the largest of two values, or taking averages, or composing two ideas together, etc. In effect, we would be mathematically modeling their aesthetic preferences. The purpose of this project is to test the feasibility of using our HR automated mathematical theory formation software for this modeling process. HR has had much success with the invention of mathematical concepts, and the discovery of theorems of genuine interest to mathematicians in number theory, graph theory and various algebraic domains. Moreover, it has recently been used to invent mathematical concepts which approximate the value of scenes for a computer art application. This small case study has highlighted the potential benefits of using HR in graphic design applications.We are proposing a two stage approach to integrating HR with graphic design software so that the user employs the software, oblivious to HR running in the background. We assume that the user starts the session with only a vague idea of what artefact they would like to produce, and are happy to work in an objet trouve (found art) manner. That is, they will look for artefacts within those presented to them for inspiration, and then pursue any ideas they have vigorously. In the first stage, we will replace the usual ad-hoc browsing of possibilities with a themed approach, where we will use HR to invent methods (fitness functions) for evaluating artefacts and then present the user with artefacts which score well within each theme. In the second stage, we will use closed-loop learning to mathematically model an approximation of the user's aesthetic. When the model is sufficiently good, it will be used to guide the user more quickly to a final artefact.We will test this approach in three application domains, namely evolutionary art, image retrieval and city design for video games. We have assembled a team of project partners with great experience in these areas, and we have software available with which to integrate HR. We plan to pay subjects to perform experiments, and we will test whether they are more efficient and/or more creative with the enhanced application software. This is a risky project, but if it is successful, it may lead to a radical change in the way designers take advantage of intelligent software in the creative process.