A Metacognitive Classifier Using a Hybrid ACT-R/Leabra Architecture

The major limitation to standard classification techniques is that the classifiers have to be trained on objects for which the ground truth, ACT-R contains a robust declarative memory module, which in terms of either a pre-assigned label or an error signal, is stores information as "chunks." A chunk in ACT-R may contain known. This limitation prevents the classifiers from dynamically any number of slots and values for those slots; slot values developing their own categories of classification based may be other chunks, numbers, strings, lists, or generally on information obtained from the environment. Previous attempts any data type allowed in Lisp (the base language for to overcome these limitations have been based on ACT-R). Retrieval from declarative memory is handled by a classical machine learning algorithms (Modayil and Kuipers request to the retrieval module; the request specifies the conditions 2007) (Kuipers et al. 2006). Here we present an alternative to be met in order for a chunk to be retrieved from approach to this problem, and develop the beginnings of declarative memory, and the module either returns a chunk a framework within which a classifier can evolve its own matching those specifications or generates a failure signal if representations based on dynamical information from the a retrieval cannot be made.