Title

A Case Study On The Critical Role Of Geometric Regularity In Machine Learning

Abstract

An important feature of many problem domains in machine learning is their geometry. For example, adjacency relationships, symmetries, and Cartesian coordinates are essential to any complete description of board games, visual recognition, or vehicle control. Yet many approaches to learning ignore such information in their representations, instead inputting flat parameter vectors with no indication of how those parameters are situated geometrically. This paper argues that such geometric information is critical to the ability of any machine learning approach to effectively generalize; even a small shift in the configuration of the task in space from what was experienced in training can go wholly unrecognized unless the algorithm is able to learn the regularities in decision-making across the problem geometry. To demonstrate the importance of learning from geometry, three variants of the same evolutionary learning algorithm (NeuroEvolution of Augmenting Topologies), whose representations vary in their capacity to encode geometry, are compared in checkers. The result is that the variant that can learn geometric regularities produces a significantly more general solution. The conclusion is that it is important to enable machine learning to detect and thereby learn from the geometry of its problems. Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Publication Date

12-24-2008

Publication Title

Proceedings of the National Conference on Artificial Intelligence

Volume

2

Number of Pages

628-633

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

57749184080 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/57749184080

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