title: Learning of embodied interaction dynamics with recurrent neural networks: some exploratory experiments.
abstract: The new tendency of artificial intelligence suggests that intelligence must be seen as a result of the interaction between brains, bodies and environments. This view implies that designing sophisticated behaviour requires a primary focus on how agents are functionally coupled to their environments. Under this perspective, we present early results with the application of reservoir computing as an efficient tool to understand how behaviour emerges from interaction. Specifically, we present reservoir computing models, that are inspired by imitation learning designs, to extract the essential components of behaviour that results from agent-environment interaction dynamics. Experimental results using a mobile robot are reported to validate the learning architectures.
authors.affiliation: Institute of Neural Information Processing, Ulm University, Germany.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Oubbati
authors.forename: Bahram
authors.initials: B
authors.lastname: Kord
authors.forename: Petia
authors.initials: P
authors.lastname: Koprinkova-Hristova
authors.forename: Günther
authors.initials: G
authors.lastname: Palm
mesh_terms: D001185:Artificial Intelligence; D005106:Exploratory Behavior; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D012371:Robotics / Q000295:instrumentation / Q000379:methods*