The general goal of the Mind RACES project is to investigate different anticipatory cognitive mechanisms and architectures in order to build Cognitive Systems endowed with the ability to predict the outcome of their actions, to build a model of future events, to control their perception anticipating future stimuli and to emotionally react to possible future scenarios. Such Anticipatory Cognitive Systems will contribute to the successful implementation of the desired ambient intelligence. 

After 3 years of research (see the Final EU Commission Report) the 7 Partners of MindRACES Consortium produced more than 80 publications, different robotic and software artefacts and important events, such as ABIALS 2008 and the Fall Symposium 2005, that show the importance of the Anticipation in various cognitive context.

This is a list of some of the most relevant publications resulting from MindRACES (click HERE to see them all):

1. Balkenius, C. and Johansson, B. (2007). Anticipatory Models in Gaze Control: A Developmental Model. Cognitive Processing, 8, 167-174.
2. Butz, M. V., Sigaud, O., Pezzulo, G., & Baldassarre, G. (Eds.). (2007). Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior, LNAI 4520 (State-of-the-Art Survey). Springer Verlag, Berlin-Heidelberg.
3. Butz, M.V., Herbort, O., & Hoffmann, J. (2007). Exploiting Redundancy for Flexible Behavior: Unsupervised Learning in a Modular Sensorimotor Control Architecture. Psychological Review, 114, 1015-1046.
4. Butz, M.V., Lanzi, P.L., & Wilson, S.W. (in press). Function Approximation with XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction. IEEE Transactions on Evolutionary Computation.
5. Gomez F. and Schmidhuber J. (2005). Co-Evolving Recurrent Neurons Learn Deep Memory POMDPs. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-05, Washington, D.C.). Nominated for Best Paper in Coevolution
6. Lorini, E., Castelfranchi, C. (2006). The unexpected aspects of Surprise. International Journal of Pattern Recognition and Artificial Intelligence, 20 (6), pp. 817-835.
7. Pezzulo, G. and Castelfranchi, C. (2007). The symbol detachment problem. Cognitive Processing, 8(2):115–131.
8. Pezzulo, G.; Butz, M.V.; Castelfranchi, C. & Falcone, R. (eds.) The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems. Springer, forthcoming
9. Schmidhuber J., Wierstra, D. and Gomez, F. (2005). Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Learning. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-05, Edinburgh).
10. Wierstra D., Gomez, F. and Schmidhuber, J. (2005). Modeling Systems with Internal State using Evolino. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-05, Washington, D.C.). Winner of Best Paper Award in Learning Classifier Systems and Other Genetics-Based Machine Learning.
11. Ognibene, D., Balkenius, C., Baldassarre, G. (in press 2008). Integrating epistemic action (active vision) and pragmatic action (reaching): a neural architecture for camera-arm robots. In: 'From Animals to Animats 10: The 10th International Conference on the Simulation of Adaptive Behaviour (SAB'08)'. 7-12 July 2008, Osaka, Japan.