Developing Credible Social Cognitive Agents – Linguistic Behavior, Simulations, Data-Driven Input, and Turing’s Legacy

Abstract

Barring swarm robotics, a substantial share of current machine-human and machine-machine learning and interaction mechanisms are being developed and fed by results of agent-based computer simulations, game-theoretic models, or robotic experiments based on a dyadic communication pattern. Yet, in real life, humans no less frequently communicate in groups, and gain knowledge and take decisions basing on information cumulatively gleaned from more than one single source. These properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with//learn from more than one contact or ‘neighbor’. To this end, significant practical import can be gleaned from research applying strict science methodology to phenomena humanistic and social, e.g. to discovery of realistic creativity potential spans, or the ‘exposure thresholds’ after which new information could be accepted by a cognitive agent. Whether in order to mimic them, or to ‘enhance’ them, parameters gleaned from complexity science approaches to humans’ social and humanistic behavior should subsequently be incorporated as points of reference in the field of robotics and human-machine interaction.