This set of tutorials demonstrates building a variety of different ACT-R models, using different aspects of ACT-R. This is meant to both give an example of how these different modules are used and to demonstrate the many different approaches that can be taken when building ACT-R models.
To simplify matters, all of the models demonstrated in this tutorial are models of Rock-Paper-Scissors. This work is based on our research in this field, where various different models were compared against real human performance. This was one of the first uses of the Python ACT-R project. The following publications are available for more information:
- West, R.L., Stewart, T.C., Lebiere, C., Chandrasekharan, S. (2005). Stochastic Resonance in Human Cognition: ACT-R Versus Game Theory, Associative Neural Networks, Recursive Neural Networks, Q-Learning, and Humans. Proceedings of the 27th Annual Meeting of the Cognitive Science Society.
- Rutledge-Taylor, M. F., West, R. L. (2005). ACT-R versus neural networks in rock=2 paper, rock, scissors. Proceedings of the Twelfth Annual ACT-R Workshop.