Abstract:
The plannln g problem has traditionally been treated separately from the scheduling problem. However, as more realistic domains are tackled, it becomes evident that the problem of deciding on an ordered set of tasks to achieve a set of goals cannot be treated independently of the problem of actually allocating resources to the tasks. Doing so would result in losing the robustness and flexibility needed to deal with imperfectly modeled domains. Completable scheduling is an approach which integrates the two problems by allowing an a priori planning module to defer particular planning decisions, and consequently the associated scheduling decisions, until execution time. This allows a completable scheduling systemto maximize plan flexibility by allowing rantime information to be taken into consideration when making planning and scheduling decisions. Furthermore, through the criterion of achievability placed on deferred decisions, a completable scheduling system is able to retain much of the goal--directedness and guarantees of achievement affordedby a priori planning. The eompletable scheduling approach is further enhanced by the use of contingent explanation--based learning, which enables a completable scheduling system to learn general completable plans from example and improve its performance through experience. Initial experimental results show that completable scheduling outperforms classical scheduling as well as pure reactive scheduling in a simple scheduling domain.