Emergence of Field Intelligence for Collective Block Agents

This paper describes the collective behavior of block agents existing in a spatially constrained environment. The difficulty of this problem is the determination of the behavior of a block agent within an environment requiring mutual action of many block agents. This difficulty is caused by dynamic obstacle avoidance problem resulting from physical collisions among the autonomous motion of block agents. The objective of this research is to build an adaptive decision mechanism of behavior for autonomous block agents when they are given a task. Specifically, this paper shows one case study of the proposed mechanism for the problem of removing blocks from a container. It is assumed that agents are block shaped, have changeable postures, and they are existing in the automated warehouse. Our approach is uses Classifier System based architecture. The results of simulation experiments are presented which indicate the possibility of this architecture in allowing block agents to adapt to dynamic environments.