Abstract
We present a biologically motivated computational model that is able to anticipate and evaluate multiple hypothetical sensorimo-tor sequences. Our Model for Anticipation based on Cortical Representations (MACOR) allows a completely parallel search at the neocortical level using assemblies of rate coded neurons for grouping, separation, and selection of sensorimotor sequences. For a vision-controlled local navigation of a mobile robot Khepera, we can demonstrate that our anticipative approach outperforms a reactive one. We also compare our explicitely planning approach with the implicitely planning Q-learning.
This work was partially supported by DFG Graduate college GRK 164.
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Heinze, A., Gross, H.M. (2001). Anticipation-Based Control Architecture for a Mobile Robot. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_124
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DOI: https://doi.org/10.1007/3-540-44668-0_124
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