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Programmable matter methods

机译：可编程物质方法

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摘要

Fine-grained, indefinitely-extended mesh architectures, which can aptly be termed `Programmable Matter', play a complementary rather than competitive role vis-a-vis traditional architectures. Ideal areas of applicability are physical modeling, materials science, interactive modeling of complex objects, image processing, and pattern recognition. Moreover, these architectures are well suited t furnish a viable blueprint for computers built out of atomic-scale components, towards which technology is inexorably leading. In most cases, its raw computational resources do not directly match the structure of problems one might want to apply it to. Since much ad hoc programming is needed to attain a reasonable fraction of the theoretical performance, so far only niche applications have been explored. We are developing a modeling methodology that will give programmable matter a much broader scope of application. This methodology makes extensive use of synthetic dynamics inspired by physics (kinematic transformations, cellular automata and lattice gases, statistical-mechanical ensembles, simulated annealing, simulated staining, texture-locked loops, etc.), but harnesses these dynamics to data-processing tasks of a more general nature as are encountered in a variety of mundane applications. In particular, by means of suitable feedback, the massive pattern-generation resources of cellular automata machines are used to construct flexible pattern recognizers.
机译：细粒度，无限扩展的网格体系结构可以恰当地称为“可编程事项”，与传统体系结构相比，起着补充而非竞争的作用。适用范围的理想领域是物理建模，材料科学，复杂对象的交互式建模，图像处理和模式识别。此外，这些体系结构非常适合为由原子级组件构建的计算机提供可行的蓝图，技术在此方面势不可挡。在大多数情况下，其原始计算资源与人们可能希望将其应用于的问题的结构不直接匹配。由于需要大量的临时编程才能达到理论性能的合理分数，因此到目前为止，仅研究了利基应用。我们正在开发一种建模方法，它将使可编程物质的应用范围更加广泛。这种方法学广泛地利用了受物理学启发的合成动力学（运动学转换，细胞自动机和晶格气体，统计力学合奏，模拟退火，模拟染色，纹理锁定环等），但是将这些动力学用于数据处理任务。如在各种普通应用中遇到的那样，具有更一般的性质。特别地，借助于适当的反馈，蜂窝自动机机器的大量模式生成资源被用于构建灵活的模式识别器。

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