Generic Tasks of Scientific Discovery

对科学发现进行建模。在过去的几年里，这个团队开发了几个计算机程序，使生物、化学、物理以及普通实验科学中的科学发现的重要方面自动化。他们通过研究科学文章等主要来源，与广泛的科学家交谈，甚至在一些科学领域进行“实地工作”，确定了有前途的发现任务。自动化方法包括启发式搜索、算法和优化的概念和技术，以及基础数学和统计学。各个领域的具体结果支持了这样一种假设，即今天很大一部分科学推理可以实现自动化。然而，有一种假设认为，大量的科学推理是由非常具体的一般性任务组成的，但从它们的计算本质来看，这是多门科学共同的。泛型科学任务的概念是生物学、计算化学等领域看待计算的一种新思路。本研究通过考察“一般科学任务”是否会产生实际结果(科学家的强大发现工具)，以及这如何为科学推理提供更系统的理解，来探索“一般科学任务”的概念。

In modeling scientific discovery. Over the last several years, this group has developed several computer programs that automate important aspects of scientific discovery in biology, chemistry, physics, and also general experimental science. They have identified promising discovery tasks by studying primary sources such as scientific articles, talking with a broad array of scientists, and even doing "field work" in some science. Automation methods have included the concepts and techniques of heuristic search, of algorithms and optimization, and a elementary mathematics and statistics. Concrete results in various fields back up the hypothesis that a significant fraction of scientific inference can be automated today. There is, however, the hypothesis that mush of scientific inference consists of generic tasks that are quite specific, but that - in their computational essence - are common to multiple sciences. The concept of generic scientific task is a new way to view computing in biology, computational chemistry, and so on. This research explore the concept of "generic scientific task" by investigating whether it leads to practical results (powerful discovery tools for scientists), and how this can provide a more systematic understanding os scientific inference.