Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Unified theories of cognition
The general utility problem in machine learning
Proceedings of the seventh international conference (1990) on Machine learning
Eliminating redundancy in explanation-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
Production matching for large learning systems
Production matching for large learning systems
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Long-term learning in soar and its application to the utility problem
Long-term learning in soar and its application to the utility problem
Human-Computer Interaction
The effect of rule use on the utility of explanation-based learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Open-ended category learning for language acquisition
Connection Science - Language and Robots
Embodied Language Acquisition: A Proof of Concept
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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What are the characteristics of long-term learning? We investigated the characteristics of long-term, symbolic learning using the Soar and ACT-R cognitive architectures running cognitive models of two simple tasks. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. We examined whether symbolic learning continues indefinitely, how the learned knowledge is used, and whether computational performance degrades over the long term. We report three findings. First, in both systems, symbolic learning eventually stopped. Second, learned knowledge was used differently in different stages but the resulting production knowledge was used uniformly. Finally, both Soar and ACT-R do eventually suffer from degraded computational performance with long-term continuous learning. We also discuss ACT-R implementation and theoretic causes of ACT-R's computational performance problems and settings that appear to avoid the performance problems in ACT-R.