Discovery of Conservation Laws via Matrix Search

Abstract. One of the main goals of Discovery Science is the development and analysis of methods for automatic knowledge discovery in the natural sciences. A central area of natural science research concerns reactions: how entities in a scientific domain interact to generate new entities. Classic AI research due to Vald´es-P´erez, ˙Zytkow, Langley and Simon has shown that many scientific discovery tasks that concern reaction models can be formalized as a matrix search. In this paper we present a method for finding conservation laws, based on two criteria for selecting a conservation law matrix: (1) maximal strictness: rule out as many unobserved reactions as possible, and (2) parsimony: minimize the L1-norm of the matrix. We provide an efficient and scalable minimization method for the joint optimization of criteria (1) and (2). For empirical evaluation, we applied the algorithm to known particle accelerator data of the type that are produced by the Large Hadron Collider in Geneva...

• Type: Journal
• Year: 2010
• Where: DIS
• Authors: Oliver Schulte, Mark S. Drew
• Keywords: Automatic Knowledge Discovery, Conservation Laws, DIS 2010, Natural Science, Theoretical Computer Science