Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
AMORD explicit control of reasoning
Proceedings of the 1977 symposium on Artificial intelligence and programming languages
Toward design as collaboration
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Model-based diagnosis of planning failures
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
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Learning how to make decisions in a domain is a critical aspect of intelligent planning behavior. The ability of a planner to adapt its decision-making to a domain depends in part upon its ability to optimize the tradeoff between the sophistication of its decision procedures and their cost. Since it is difficult to optimize this tradeoff on a priori grounds alone, we propose that a planner start with a relatively simple set of decision procedures, and add complexity in response to experience gained in the application of its decision-making to real-world problems. Our model of this adaptation process is based on the explanation of failures, in that it is the analysis of bad decisions that drives the improvement of the decision procedures. We have developed a test-bed system for the implementation of planning models employing such an approach, and have demonstrated the ability of such a model to improve its procedure for projecting the effects of its moves in chess.