Computationally Efficient Approximation of the Base-Level Learning Equation in ACT-R

Petrov, A. (2006)
Computationally efficient approximation of the base-level learning equation in ACT-R. In D. Fum, F. Del Missier, & A. Stocco (Eds.), Proceedings of the Seventh International Conference on Cognitive Modeling (pp. 391-392). Trieste, Italy: Edizioni Goliardiche.
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Brief Description:

The base-level equation is one of the pillars of ACT-R's success. It has a serious practical drawback, however -- it consumes a lot of memory and CPU cycles. The approximate formula published in Anderson and Lebiere's (1998) book is good for many purposes but does not capture the transient boost after each use of a chunk. This two-page abstract describes an improved approximation that does take all critical properties of the exact equation into account. It was used with great success in the ANCHOR model of Petrov & Anderson (2005). It can be very useful for large simulations, allowing ACT-R to scale up to more realistic memory sizes and more prolonged learning periods. Finally, its constituent terms map naturally to the biologically realistic distinction between weight-based and activation based memories.

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Created 2006-03-22, last updated 2006-12-12.