数字突触神经基底：计算创造力的新途径
The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity
摘要：
我们介绍了一种新的人工智能（AI）方法，称为“数字突触神经基质”（DSNS）。它使用来自各个领域（例如国际象棋问题、古典音乐、著名艺术品）的对象的选定属性，并以生成新属性的方式对它们进行重组，然后原则上可以使用这些新属性来创建具有创造价值的新颖对象与任何一个源领域相关的人类。这使得创造性内容生成的一些负担从人类转移到机器上。该方法进行了测试，在国际象棋的问题组成的域。我们用它来自动组成许多集的国际象棋问题的基础上提取和重组的属性，从国际象棋问题和锦标赛游戏的人类，著名的绘画，计算机进化的抽象艺术，照片的人，和古典音乐曲目。这些生成的国际象棋问题的质量，然后使用现有的和实验验证的计算国际象棋美学模型自动评估。它们也由该领域的人类专家进行评估。结果表明，收集和重组的属性从国际象棋和其他领域使用DSNS的方法确实可以用来自动生成国际象棋的问题，合理的高美学质量。特别是，一个低质量的国际象棋源（即弱玩家之间的比赛游戏序列）与人的实际照片相结合使用，能够产生质量相当或更好的三步国际象棋问题，使用高质量的国际象棋源（即人类专家发表的作品），以及更有效。为什么来自外国领域的信息可以以这种方式整合和发挥作用，这仍然是一个悬而未决的问题。原则上，DSNS方法是可伸缩的，并且适用于对象具有可以使用实数表示的属性的任何域。
英文摘要：
We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
