Using colored petri nets to predict future states in agent-based scheduling and planning systems

Abstract

In Agent Based Scheduling and Planning Systems, autonomous agents are used to execute scheduling/planning tasks on behalves of represented enterprises. As application domains become more and more complex, agents are required to handle a number of changing and uncertain factors. This makes it necessary to embed state prediction mechanisms in Agent Based Scheduling and Planning Systems. In this paper, a Colored Petri Net based approach for supporting automated scheduling and planning is introduced. In the approach, we adopt an augmentation Colored Petri Net model which can not only analyse future states of a system but also estimate the success probability of reaching a particular future state. By using augmentation Colored Petri Nets to model relative dynamic factors in scheduling/planning problems, agents can predict the probable future states of a system and corresponding risks of reaching those states. The proposed approach can enable agents to make more rational and accurate decisions in complex scheduling and planning problems.