Abstract
This paper extends prior work on knowledge consolidation and the stability-plasticity problem within the context of a Lifelong Machine Learning (LML) system. A context-sensitive multiple task learning (csMTL) neural network is used as a consolidated domain knowledge store. Prior work has demonstrated that a csMTL network, in combination with task rehearsal, can retain previous task knowledge when consolidating a sequence of up to ten tasks from a domain. However subsequent experimentation has shown that the method suffers from scaling problems as the learning sequence increases resulting in the loss of prior task accuracy and a growing computational cost for rehearsing prior tasks using larger training sets. A solution to these two problems is presented that uses a sweep method of rehearsal that requires only a small number of rehearsal examples (as few as one) for each prior task per training iteration in order to maintain prior task accuracy.
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Silver, D.L., Mason, G., Eljabu, L. (2015). Consolidation Using Sweep Task Rehearsal: Overcoming the Stability-Plasticity Problem. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_27
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DOI: https://doi.org/10.1007/978-3-319-18356-5_27
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