摘要翻译:
进化艺术和设计算法的一个可感知的局限性是,它们依赖于人类的干预;艺术家选择一代最美观的变体来制作下一代。本文讨论了计算机生成的艺术和设计如何在过程和结果方面变得更有创造性地像人。作为这方面的一个例子,我们提出了一种通过使用自动适应度函数来克服上述限制的算法。我们的目标是进化出达尔文的抽象画像,使用我们的第二代适应度函数,该函数奖励那些不仅产生达尔文的相似性,而且表现出人类艺术家特有的某些策略的基因组。我们注意到,在人类的创造力中,变化不是在随机产生的变体中选择,而是更多地利用概念网络的联想结构来磨练愿景。我们讨论了如何在算法上实现这种流动性。
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英文标题:
《Incorporating characteristics of human creativity into an evolutionary
art algorithm》
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作者:
Steve DiPaola and Liane Gabora
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最新提交年份:
2019
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Neural and Evolutionary Computing 神经与进化计算
分类描述:Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络,连接主义,遗传算法,人工生命,自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类:Quantitative Biology 数量生物学
二级分类:Neurons and Cognition 神经元与认知
分类描述:Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触,皮层,神经元动力学,神经网络,感觉运动控制,行为,注意
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英文摘要:
A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
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PDF链接:
https://arxiv.org/pdf/1001.1401