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Cognitive modeling with context sensitive reinforcement learning

In English

Författare

Redaktör

  • J. Malek

Summary, in English

We describe how a standard reinforcement learning algorithm can be changed to include a second contextual input that is used to modulate the learning in the original algorithm. The new algorithm takes the context into account during relearning when the previously learned actions are no longer valid. The algorithm was tested on a number of cognitive experiment and shown to reproduce the learning in both a task switching test and in the Wisconsin Card Sorting Test. In addition, the algorithm was able to learn a context sensitive categorization of objects in the Labov experiment.

Publiceringsår

2004

Språk

Engelska

Sidor

10-19

Publikation/Tidskrift/Serie

Proceedings of AILS 04 ( Report / Lund Institute of Technology, Lund University ; 151)

Dokumenttyp

Konferensbidrag

Förlag

Department of Computer Science, Lund University

Ämne

  • Computer Vision and Robotics (Autonomous Systems)

Status

Published

Projekt

  • Ikaros: An infrastructure for system level modelling of the brain

ISBN/ISSN/Övrigt

  • ISSN: 1650-1276