Modelling Multiple Robots in Space: An Adaptive Eco-Grammar System

Abstract

We present a multi-robot model suitable for study of interactions and emergence of rational behavior. We focus on a grammatical approach, and to demonstrate its advantages, we design a model of an adaptive multi-robot community in terms of eco-grammar systems. We show that this grammatical model, based on the blackbord architecture, can naturally involve reinforcement collective learning. We test two learning algorithms in a common environment with almost reactive co-operating robots. Experimental results show that using the grammatical model, the robot community can be successfully trained to find a close-to-optimal solution to a given NP-complete task of a truss construction.