Behavioral Coordination, Structural Congruence and Entrainment in a Simulation of Acoustically Coupled Agents

Author:

Di Paolo Ezequiel A.1

Affiliation:

1. University of Sussex, Brighton BNI 9QH, U.K.

Abstract

Social coordination is studied in a simulated model of autonomous embodied agents that interact acoustically. Theoretical concepts concerning social behavior are presented from a systemic per spective and their usefulness is evaluated in interpreting the results obtained. Two agents moving in an unstructured arena must locate each other, and remain within a short distance of one anoth er for as long as possible using noisy continuous acoustic interaction. Evolved dynamical recurrent neural networks are used as the control architecture. Acoustic coupling poses nontrivial problems like discriminating 'self' from 'non-self' and structuring production of signals in time so as to minimize interference. Detailed observation of the most frequently evolved behavioral strategy shows that interacting agents perform rhythmic signals leading to the coordination of movement. During coordination, signals become entrained in an anti-phase mode that resembles turn-taking. Perturbation techniques show that signalling behavior not only performs an external function, but it is also integrated into the movement of the producing agent, thus showing the difficulty of separating behavior into social and non-social classes. Structural congruence between agents is shown by exploring internal dynamics as well as the response of single agents in the presence of signalling beacons that reproduce the signal patterns of the interacting agents. Lack of entrainment with the signals produced by the beacons shows the importance of transient periods of mutual dynamic perturbation wherein agents achieve congruence.

Publisher

SAGE Publications

Subject

Behavioral Neuroscience,Experimental and Cognitive Psychology

Reference41 articles.

1. Beer, R.D. (1995). Computational and dynamical languages for autonomous agents. In R. Port, & T. van Gelder (Eds.), Mind as Motion: Explorations in the Dynamics of Cognition, pp.121-147. Cambridge, MA: MIT Press.

2. Evolving Dynamical Neural Networks for Adaptive Behavior

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