Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of problem solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually solve. Among the advantages of the MAIS, when compared to the centralized systems, are increased reliability, faster problem solving, decreased communication, and more flexibility. Learning to coordinate the actions is one of the most important task in MAIS. In the current research, we use a widely reported dynamic job shop scheduling simulation model that uses distributed genetic learning of job scheduling strategies (Pendharkar, P.C., 1997. Doctoral Dissertation, Graduate School, Southern Illinois University at Carbondale; Pendharkar, P.C., 1998. Distributed learning of objectives for adaptive scheduling (in review); Pendharkar, P.C., Bhattacharyya, S., 1997. Multi-agent learning in distributed artificial intelligence. Proc. 2nd INFORMS Conference on Information Systems and Technology. San Diego, CA, p.156-163; Bhattacharyya, S., Koehler, G.J., 1997. Learning by objectives for adaptive shop-floor learning. Decision Sciences (to appear). Aytug, H., Koehler, G.J., Snowdon, J.L., 1994. Genetic learning of dynamic scheduling within a simulation environment, Computers and Operations Research, 21 (8), 909-925; Aytug, H., Bhattacharyya, S., Koehler, G.J., Snowdon, J.L., 1994. A review of machine learning in scheduling, IEEE Transactions on Engineering Management 41 (2) ) and study the performance and design issues in multi-agents information systems for dynamic scheduling in manufacturing. Among the design issue and performance issues considered in this research are coordination between agents, number of agents, and frequency of learning. Our results indicate that coordination between agents, and learning frequency play a significant role in the performance of multi-agent intelligent systems.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence