Abstract
This work proposes a new method to model the extrinsic motivation of a cognitive architecture based on the discovery of separable utility regions (SUR), which reduce the complexity of the standard value functions typically used. Those regions exhibit a correlation between the expected utility and the response of one sensor of the robot. Once they are discovered, the evaluation of the candidate states is only based on the changes of one sensor, which provides a strong independence from noise or dynamism in the utility models. A non-static variation of the classical collect-a-ball scenario was used to test the mechanism in order to generate and define the certainty maps associated to those SURs. Preliminary results show a good response of the technique and a clear improvement in performance when this is associated to a restructuring mechanism for the utility model, which, in this case, corresponds to the creation and chaining of sub-goals.
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Acknowledgments
This work has been partially funded by the EU’s H2020 research and innovation programme under grant agreement No. 640891 (DREAM project) and by the Xunta de Galicia and redTEIC network (ED341D R2016/012).
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Salgado, R., Prieto, A., Bellas, F., Duro, R.J. (2017). Motivational Engine for Cognitive Robotics in Non-static Tasks. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_4
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DOI: https://doi.org/10.1007/978-3-319-59740-9_4
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