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Nate Derbinsky Wins Best Poster Award at ICCM

Ph.D. candidate Nate Derbinsky has won the Best Poster Award at the 11th International Conference on Cognitive Modeling (ICCM), which took place April 13 - 15 in Berlin, Germany.
Nate Derbinsky Enlarge
Nate Derbinsky

Ph.D. candidate Nate Derbinsky has won the Best Poster Award at the 11th International Conference on Cognitive Modeling (ICCM), which took place April 13 – 15 in Berlin, Germany.

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The award is for the poster “Computationally Efficient Forgetting via Base-Level Activation,” which outlines research that Mr. Derbinsky is conducting with Prof. John E. Laird. The work is also described in this paper.

The poster and paper address the need to remove declarative knowledge from memory, “forgetting it,” as models of human cognition are applied to complex, temporally extended tasks. They describe how to efficiently remove items from memory, while preserving base-level activation (BLA) model fidelity. The BLA model predicts that the availability of specific memories is sensitive to frequency and recency of past usage.

Artificial Intelligence; Honors and Awards; John Laird; Machine Learning; Student News