Genetic-based agents for control of distributed systems

Management and control of distributed systems are hard tasks for conventional control techniques. Distributed computer control systems (DCCS) and data communication technology have helped to alleviate some of the problems of distributed systems. However, there are some problems that are difficult to solve using conventional methods. Artificial intelligence (AI) techniques have been proposed as solutions to many of the problems inherent in distributed systems. These solutions sometimes prove too complex to use in real systems, and a simpler adaptive system may be needed. This paper discusses adaptive systems from the genetic-based machine learning paradigm, and how they can be integrated with distributed artificial intelligence techniques for the control of distributed systems