The teachable language comprehender: a simulation program and theory of language
Communications of the ACM
A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Learning Structural Descriptions From Examples
Learning Structural Descriptions From Examples
Decision Tree Learning Using a Bayesian Approach at Each Node
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Mining multidimensional contextual outliers from categorical relational data
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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ACT is a computer simulation program that uses a propositional network to represent knowledge of facts and a set of productions (condition - action rules) to represent knowledge of procedures. There are currently four different mechanisms by which ACT can make additions and modifications to its set of productions: designation, strengthening, generalization, and discrimination. Designation refers to the ability of productions to call for the creation of new productions. Strengthening a production involves adjusting the amount of system resources that will be allocated to its processing. Finally, generalization and discrimination refer to complementary processes that produce better performance by either extending or restricting the range of situations in which a production will apply. Theae learning mechanisms are used to simulate experiments on prototype formation. ACT successfully accounts for the effects of distance of instances from a central tendency, frequency of individual instances, and the family resemblance structure of categories.