Dynamically reconfigurable intelligent system of cellular robotic system (CEBOT) with entropy min/max hybrid algorithm

This paper discusses a framework and the algorithms for a reconfigurable intelligent system, which is capable of “knowledge amplification”. The proposed intelligent system consists of many kinds of knowledge sources, which have simple levels of data and intelligence together with some blackboards which dynamically organize a hierarchical structure in order to optimize its own performance index. The system has two strategies for reconfiguration of the system structure: one is “entropy maximum strategy” the other is “ entropy minimum strategy”. The entropy maximum strategy is used when many kinds of tasks are given to the system. The entropy minimum strategy is applied in cases were the system deals with a few tasks only. This paper addresses the fundamental configuration and algorithms which include these two strategies for reconfiguration. The efficiency of the proposed system is shown by some simulations