Algorithms and Inference of Grammars and Natural Computing Models

语法和自然计算模型的算法和推理

• 批准号: RGPIN-2022-05092
• 负责人: McQuillan, Ian
• 金额: $ 2.99万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749865
• 关键词: Algorithms; Inference; Grammars; Natural; Computing

When plant breeders create new varieties, they inspect thousands of plants looking for desirable or undesirable growth, and evidence of stress or disease. This has traditionally been done by manual inspection which forms a bottleneck that can be alleviated with automatic image analysis. Machine learning can be used to detect various components and their form; it is common to start with a large set of images with these properties already identified, and use them to train a model that maps inputs to outputs. Artificial neural networks are often used for this, but it is notoriously difficult to extract scientific knowledge from them. Formal grammars are a mathematical formalism with rules for rewriting strings that are repeatedly applied, starting from an initial symbol to create new strings. Lindenmayer systems (L systems) are a type of grammar system that were created to model multicellular structures present in many biological organisms with inherent self-similarity, such as plants. L systems have been widely used to create realistic visual simulations of developing plants. The first objective is to use L systems (and not e.g. neural networks) for predicting plant components and geometry from sequences of developing plant images. An existing L system created to capture a range of phenotypes (e.g. a species) will be used as a starting point. Image processing will be used as an initial prediction of plant components on each image. Then, software will be created to find the L system simulation that most closely matches the initial prediction. This simulation will form a new prediction that must be consistent with the developmental program of the plant (in contrast to predictions from images analyzed independently). This will detect some components even if they are completely hidden in an image. The accuracy will be compared to other predictive models. While this prediction does not require annotated data, it does require L systems which are also currently difficult to create, especially for each variety of interest. Hence, the second objective is to build algorithms and software to learn L systems automatically from data (images or descriptions of them). L systems can optionally have probabilities associated with rewriting rules which are used to calculate the probability of a simulation occurring. We will create software for their calculation using image data on top of the L system from the first objective. Next, software will be built to learn the rules themselves from data. The anticipated outcomes are the creation of new methods and software to use and learn L systems towards strengthening the pipeline for phenotyping and improving crops. Learned grammars and probabilities directly describe development and hence, scientific principles can be extracted and better understood. These are both critical towards increasing food security amidst a growing population and dramatic environmental changes within Canada and worldwide.

当植物育种者创造新品种时，他们会检查成千上万的植物，以寻求理想或不良的生长以及压力或疾病的证据。传统上，这是通过手动检查来完成的，该检查形成了可以通过自动图像分析来缓解的瓶颈。机器学习可用于检测各种组件及其形式。通常，从已经确定的这些属性的大量图像开始，然后使用它们来训练将输入映射到输出的模型。人工神经网络经常用于此，但是从中提取科学知识是很难的。形式的语法是一种数学形式主义，具有重写字符串的规则，从初始符号开始创建新字符串。 Lindenmayer Systems（L系统）是一种语法系统，是为了建模许多具有固有自相似性（例如植物）的生物生物体中的多细胞结构。 L系统已被广泛用于创建开发植物的现实视觉模拟。第一个目标是使用L系统（而不是神经网络）来预测开发植物图像序列的植物成分和几何形状。现有的L系统旨在捕获一系列表型（例如一个物种）作为起点。图像处理将用作每个图像上植物成分的初始预测。然后，将创建软件以找到与初始预测最匹配的L系统模拟。该模拟将形成一个新预测，必须与植物的发展程序一致（与独立分析的图像的预测相反）。即使它们完全隐藏在图像中，也会检测到某些组件。准确性将与其他预测模型进行比较。尽管此预测不需要注释的数据，但确实需要L系统，这些系统目前也很难创建，尤其是对于各种兴趣而言。因此，第二个目标是构建算法和软件，以从数据（图像或描述）自动学习L系统。 L系统可以选择具有与重写规则相关的概率，该规则用于计算发生模拟的概率。我们将使用第一个目标的L系统之上的图像数据创建用于计算的软件。接下来，将构建软件以从数据中学习规则。 预期的结果是创建新方法和软件，以使用和学习L系统来加强表型和改善作物的管道。学到的语法和概率直接描述了发展，因此，可以提取并更好地理解科学原理。在加拿大和全球范围内的人口不断增长和急剧环境变化的情况下，这些都是至关重要的。