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Robert B. Doorenbos

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AAAI Conference 1994 Conference Paper

Combining Left and Right Unlinking for Matching a Large Number of Learned Rules

Robert B. Doorenbos

In systems which learn a large number of rules (productions), it is important to match the rules efficiently, in order to avoid the machine learning utility prob- Zem. So we need match algorithms that scale well with the number of productions in the system. (Doorenbos 1993) introduced right unlinking as a way to improve the scalability of the Rete match algorithm. This paper introduces a symmetric optimization, left unlinking, and demonstrates that it makes Rete scale well on an even larger class of systems. Unfortunately, when left and right unlinking are combined in the same system, they can interfere with each other. We give a particular way to combine them which we prove minimizes this interference, and analyze the worst-case remaining interference. Finally, we present empirical results showing that the interference is very small in practice, and that the combination of left and right unlinking allows five of our seven testbed systems to learn over 100, 000 rules without incurring a significant increase in match cost. ’

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AAAI Conference 1993 Conference Paper

Matching 100,045 Learned Rules

Robert B. Doorenbos

This paper examines several systems which learn a large number of rules (productions), including one which learns 113, 938 rules - the largest number ever learned by an AI system, and the largest number in any production system in existence. It is important to match these rules efficiently, in order to avoid the machine learning utility problem. Moreover, examination of such large systems reveals new phenomena and calls into question some common assumptions based on previous observations of smaller systems. We first show that the Rete and Treat match algorithms do not scale well with the number of rules in our systems, in part because the number of rules affected by a change to working memory increases with the total number of rules in these systems. We also show that the sharing of nodes in the beta part of the Rete network becomes more and more important as the number of rules increases. Finally, we describe and evaluate a new optimization for Rete which improves its scalability and allows two of our systems to learn over 100, 000 rules without significant performance degradation. ’

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