Learning from Knowledge and Data for Ecosystem Prediction

该项目将开发和测试现有领域知识与大量训练数据相结合的方法，以产生高性能的分类系统。这些方法的成功开发将解决机器学习中的基本问题，并解决科学和工程中的关键应用问题。特别是，这些方法将在生态系统建模领域得到发展和测试，在那里它们可能能够提供更可靠的大气二氧化碳（CO2）增加对植物生态系统（生物群落）地理分布影响的估计。将研究两种方法：(a)以理论为中心的方法，其中对植物生理学的部分理论进行改进，以便预测生物群落分布；(b)在标准机器学习算法中应用以规则为中心的方法来发现生物群落分布规则。以规则为中心的方法将使用植物生理学的部分理论来提出相关特征并使所发现的规则合理化。这两种方法都需要开发新的学习方法，包括(a)用于解释数据和细化领域理论的约束推理技术，(b)用于定义候选特征的建设性归纳技术，以及(c)用于确保发现规则与领域理论的一致性的合理化技术。//

This project will develop and test methods by which existing domain knowledge can be combined with large amounts of training data to yield high-performance classification systems. The successful development of such methods would address fundamental questions in machine learning and solve critical applications problems in science and engineering. In particular, the methods will be developed and tested in the domain of ecosystem modelling, where they may be able to provide more reliable estimates of the effect of increased atmospheric carbon dioxide (CO2) on the geographical distribution of plant ecosystems (biomes). Two approaches will be investigated: (a) a theory-centered approach, in which a partial theory of plant physiology is refined so that it can predict biome distribution, and (b) a rule-centered approach in a standard machine learning algorithm is applied to discover biome distribution rules. The rule-centered approach will use the partial theory of plant physiology to suggest relevant features and to rationalize the discovered rules. Both approaches will entail the development of new learning methods including (a) constraint reasoning techniques for the interpretation of data and the refinement of the domain theory, (b) constructive induction techniques for defining candidate features, and (c) rationalization techniques for ensuring the consistency of discovered rules with the domain theory.//