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ACT-R
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===Spin-offs=== The long development of the ACT-R theory gave birth to a certain number of parallel and related projects. The most important ones are the '''PUPS production system''', an initial implementation of Anderson's theory, later abandoned; and '''ACT-RN''',<ref name="actrn"/> a neural network implementation of the theory developed by Christian Lebiere. [[Lynne M. Reder]], also at [[Carnegie Mellon University]], developed '''[[SAC (Computational model)|SAC]]''' in the early 1990s, a model of conceptual and perceptual aspects of memory that shares many features with the ACT-R core declarative system, although differing in some assumptions. For his dissertation at [[Carnegie Mellon University]], [[Christopher L. Dancy]] developed, and successfully defended in 2014, '''ACT-R/Phi''',<ref>Dancy, C. L., Ritter, F. E., & Berry, K. (2012). Towards adding a physiological substrate to ACT-R. In ''21st Annual Conference on Behavior Representation in Modeling and Simulation 2012, BRiMS 2012'' (pp. 75-82). (21st Annual Conference on Behavior Representation in Modeling and Simulation 2012, BRiMS 2012).</ref> an implementation of ACT-R with added physiological modules which enable ACT-R to interface with human physiological processes. A lightweight Python-based implementation of the working memory component of ACT-R, '''pyACTUp''',<ref>{{cite web |last1=Morrison |first1=Don |title=pyactup |url=https://github.com/rogerlew/pyactup |website=github.com |access-date=15 September 2023}}</ref> was created by Don Morrison at [[Carnegie Mellon University]], who maintains the ACT-R codebase. This library implements ACT-R as a unimodal [[supervised learning]] model for classification tasks.
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