Programming expert systems in OPS5: an introduction to rule-based programming
Programming expert systems in OPS5: an introduction to rule-based programming
SOAR: an architecture for general intelligence
Artificial Intelligence
Partitioning in parallel processing of production systems
Partitioning in parallel processing of production systems
Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Unified theories of cognition
Learning approximate control rules of high utility
Proceedings of the seventh international conference (1990) on Machine learning
A preliminary analysis of the Soar architecture as a basis for general intelligence
Artificial Intelligence
Eliminating combinatorics from production match
Eliminating combinatorics from production match
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Parallelism in production systems
Parallelism in production systems
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
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This paper describes an initial exploration into large learning systems, i.e., systems that learn a large number of rules. Given the well-known utility problem in learning systems, efficiency questions are a major concern. But the questions are much broader than just efficiency, e.g., will the effectiveness of the learned rules change with scale? This investigation uses a single problem-solving and learning system, Dispatcher-Soar, to begin to get answers to these questions. Dispatcher-Soar has currently learned 10, 112 new productions, on top of an initial system of 1, 819 productions, so its total size is 11, 931 productions. This represents one of the largest production systems in existence, and by far the largest number of rules ever learned by an AI system. This paper presents a variety of data from our experiments with Dispatcher-Soar and raises important questions for large learning systems.