A coordination technique in a highly automated train rescheduling system

Realistic problems are often not only complex and dynamic, but also involve various kinds of interaction among software processes, with human beings, and with a lot of distributing equipment and computers connected through communication networks. Train rescheduling is such a problem in plan revision. In this kind of problem, the AI system´s output can not be used without human acceptance. And a person such as the train dispatcher has to be responsible for the acceptance of the output. Further, situations dynamically change in real-time. Thus, the central problem here is to let him quickly grasp and accept the AI system´s suggestions. Focussing on such problems, this paper presents an AI coordination technique especially for plan revision consisting of: a flexible and hierarchical representation mechanism for goal decomposition and its alternative attainment knowledge, for solving complex problems through divide and conquer; dynamic goal generation, goal prioritizing, and its concurrent achievement through preemption, for solving dynamic problems; a fine-grained goal coordination mechanism for a real time complex system; and a man-machine coordination mechanism for quick user acceptance (rule-type knowledge representation for encapsulating multiple suggestions such as for proposing alternative suggestions together and/or for proposing subordinate suggestions together to solve side effects). This technique was used and evaluated in a train rescheduling AI system, which succeeded in its practical use for the first time in one of Japanese largest cities where trains run every 2-3 minutes. This proved that the technique could contribute to solving a real-world dynamic and complex man-in-the-loop problem