Discrete sequence prediction and its applications

Authors:
Philip Laird
Affiliations:
NASA Ames Research Center, Moffett Field, California
Venue:
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Year:
1992

Citing 8
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Abstract

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We present a simple and practical algorithm (TDAG) for discrete sequence prediction, verify its performance on data compression tasks, and apply it to problem of dynamically optimizing Prolog programs for good average-case behavior.