All Outputs (3)

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles (2018)
Presentation / Conference Contribution
Volpi, N. C., Smith, S. C., Pascoal, A. M., Simetti, E., Turetta, A., Alibani, M., & Polani, D. (2018). Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles. In OCEANS 2018 MTS/IEEE Charleston. https://doi.org/10.1109/oceans.2

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3... Read More about Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles.

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop (2018)
Journal Article
Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., & Polani, D. (2018). Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop. Frontiers in Neurorobotics, 12, https://doi.org/10.3389/fnbot.2018.00045

Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including cons... Read More about Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop.

Evaluation of Internal Models in Autonomous Learning (2018)
Journal Article
Smith, S. C., & Herrmann, J. M. (2019). Evaluation of Internal Models in Autonomous Learning. IEEE Transactions on Cognitive and Developmental Systems, 11(4), 463-472. https://doi.org/10.1109/tcds.2018.2865999

Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under pa... Read More about Evaluation of Internal Models in Autonomous Learning.