Autonomous Learning Needs a Second Environmental Feedback Loop

Toutounji, Hazem; Pasemann, Frank

Abstract

Deriving a successful neural control of behavior of autonomous and embodied systems poses a great challenge. The difficulty lies in finding suitable learning mechanisms, and in specifying under what conditions learning becomes necessary. Here, we provide a solution to the second issue in the form of an additional feedback loop that augments the sensorimotor loop in which autonomous systems live. The second feedback loop provides proprioceptive signals, allowing the assessment of behavior through self-monitoring, and accordingly, the control of learning. We show how the behaviors can be defined with the aid of this framework, and we show that, in combination with simple stochastic plasticity mechanisms, behaviors are successfully learned.

• Online Publication Date: Nov 20, 2015
• Publication Date: 2016
• Deposit Date: Jul 6, 2020
• Pages: 455-472
• Series Title: Studies in Computational Intelligence
• Series Number: 613
• Book Title: Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013
• ISBN: 9783319233918
• DOI: https://doi.org/10.1007/978-3-319-23392-5_25
• Public URL: https://nottingham-repository.worktribe.com/output/4754301
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-319-23392-5_25