Cooperative autonomous agents based on dynamical fuzzy cognitive maps

Abstract

This work presents an architecture for cooperative autonomous agents based on dynamic fuzzy cognitive maps (DFCM) that are an evolution of fuzzy cognitive maps. This architecture is used to build an autonomous navigation system for mobile robotics that presents learning capacity, on line tuning, self-adaptation abilities and behaviors management. The developed navigation system adopts a multi-agent approach, inspired on the Brooks’ subsumption architecture due to its hierarchical management functions, parallel processing and direct mapping from situation to action. In this paper, a DFCM is hierarchically developed, from low-level describing reactive actions to the highest level that comprises management actions. A multi-agent scheme to share experiences among robots is also implemented at the last hierarchy level based on pheromone exchange by ant colony algorithm. The proposed architecture is validated on a simple example of swarm robotics.