COADAPTIVE BEHAVIOR IN A SIMPLE DISTRIBUTED JOB SCHEDULING SYSTEM

A simple system that demonstrates coadaptation in distributed decision-making as applied to the problem of job scheduling is presented. The decision-making process is constructed as a set of autonomous agents based on learning automata that can individually learn from feedback. The set of agents adapt together, or coadapt, to form a good, though not necessarily optimal, global decision process. Coadaptive distributed computer job scheduling in a two-machine system is simulated with the agents implemented as stochastic learning automata. It is demonstrated that the performance of these coadaptive agents is similar to that of a related group of well-performing static decision-making agents, that quantitative changes in agents' parameters cause qualitative changes in the coadaptive behavior, and that constructing an optimal agent for a coadapting system is dependent upon the other agents present. Coadaptive behavior is clearly affected by the relative frequencies with which decisions are made, and the relative sizes of the rewards and penalties.