Synthetic design of a social regressor and its implementation using Knowledge Request-Broker Architecture

By thinking of a society as a cognitive system, this paper proposes a theoretical framework for artificial social intelligence. This framework is implemented using Knowledge Request-Broker Architecture, and its applicability is experienced in the field of system identification by letting a society of self-interested agents, with various skills, to identify a system based on given training data. To develop a theory about how social intelligence works, two questions were asked. First, “What was changed in time that made less intelligent societies to become more intelligent?”, and second “what drives individuals to take part in social productiveness?”. The framework reported in this paper is inspired by the answers given to these question through the study of the emergence of human civilizations, and putting it beside computer and knowledge engineering concepts. The experiments with this system show how social ensemble of simple autonomous agents can solve harder problems, while demonstrating the practicality of the proposed framework and its associated API.