Abstract
Metacognitive control is a salient capability of the human cognitive processes that allows them to regulate their executive functions and improve their performance and learning. Here, we contribute towards a mathematical model of metacognitive control for linear dynamic systems with three key features i) control the system, ii) evaluate a second order judgement about the decisions made (confidence in control) and, iii) self-monitor the control confidence and adapt it to improve the controller. We show that adapting the control confidence can improve the controller performance, particularly when the agent is biased with over confidence on its prior beliefs, i.e., it is too confident about its control.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, M.L., Oates, T., Chong, W., Perlis, D.: The metacognitive loop I: enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance. J. Exp. Theor. Artif. Intell. 18(3), 387–411 (2006)
Anil Meera, A., Lanillos, P.: Towards metacognitive robot decision making for tool selection. In: International Workshop on Active Inference, pp. 31–42. Springer (2023)
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017)
Daglarli, E.: Computational modeling of prefrontal cortex for meta-cognition of a humanoid robot. IEEE Access 8, 98491–98507 (2020)
Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K.J., Ramstead, M.J.: Deeply felt affect: the emergence of valence in deep active inference. Neural Comput. 33(2), 398–446 (2021)
Kawato, M., Cortese, A.: From internal models toward metacognitive AI. Biol. Cybern. 115, 415–430 (2021)
Lanillos, P., et al.: Active inference in robotics and artificial agents: survey and challenges. arXiv preprint arXiv:2112.01871 (2021)
Lyons, K.E., Zelazo, P.D.: Monitoring, metacognition, and executive function: elucidating the role of self-reflection in the development of self-regulation. Adv. Child Dev. Behav. 40, 379–412 (2011)
Ogata, K.: Modern control engineering fifth edition (2010)
Parr, T., Pezzulo, G., Friston, K.J.: Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press, Cambridge (2022)
Taniguchi, T., et al.: World models and predictive coding for cognitive and developmental robotics: frontiers and challenges. Adv. Robot. 1–27 (2023)
Acknowledgments
This work has been funded by the EU METATOOL project funded by the EIC Pathfinder challenge Awareness Inside.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Anil Meera, A., Lanillos, P. (2024). Metacognitive Control of Linear Dynamic Systems with Self-confidence Adaptation. In: Secchi, C., Marconi, L. (eds) European Robotics Forum 2024. ERF 2024. Springer Proceedings in Advanced Robotics, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-76424-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-76424-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-76423-3
Online ISBN: 978-3-031-76424-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)